Curriculum Handbook

I.                MAIN COMPETENCY COURSES (MAC)

UIN6021204 Arabic

Module NameArabic
Module level, if applicableUndergraduate
Module Identification CodeUIN6021204
Semester(s) in which the module is taught1
Person(s) responsible for the moduleDr. Achmad Fudhaili M.Pd
LanguageIndonesian and Arabic
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursLecture, class discussion, structured activities (homework, quizzes), Collaborative Learning .
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 60 min x 14 wks = 42 h  • Independent study: 3 x 60 min  x 14 wks = 42 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 124 hours
Credit points3 Credit Hours ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites
Media employedBoard, LCD Projector, Laptop/Computer
Forms of assessmentAssignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, the students should have: Developed a fundamental understanding of the Arabic language, including grammar, vocabulary, and pronunciation. Gained proficiency in reading and comprehending simple Arabic texts and expressions. Acquired basic conversational skills to engage in everyday discussions and interactions in Arabic. Expanded their vocabulary and functional language use for various common situations in Arabic. Enhanced their listening skills to grasp and interpret spoken Arabic in various contexts. Improved their writing skills to construct simple paragraphs and express ideas coherently in Arabic. Demonstrated cultural sensitivity and awareness when using Arabic in diverse social and cultural settings. Acquired foundational knowledge of Arab culture, traditions, and societal norms related to language use. Exhibited the ability to introduce themselves and others in Arabic, and provide basic personal information. Shown proficiency in using Arabic for common activities like shopping, ordering food, giving directions, etc. Mastered the Arabic script and its application in reading and writing. Demonstrated the capability to describe people, places, and events in Arabic. Gained insights into the interconnectedness of language and culture in Arabic-speaking communities. Displayed readiness to further advance their Arabic language skills and pursue higher levels of proficiency. Successfully applied the learned language skills to practical situations, enhancing their overall Arabic language competence.
Module content
Introduction to Arabic Language and Culture Arabic Alphabet and Pronunciation Basic Arabic Vocabulary and Expressions Grammar Fundamentals Arabic Reading and Comprehension Arabic Writing Practice Conversational Arabic Arabic Vocabulary Expansion Listening and Speaking Proficiency Cultural Etiquette and Practices Intermediate Grammar and Sentence Structure Reading Comprehension and Analysis Expressing Opinions and Descriptions Role of Arabic in the Modern World Final Project and Presentation
Recommended Literatures Wehr, Hans. “Arabic-English Dictionary: The Hans Wehr Dictionary of Modern Written Arabic.” (Edisi Keempat, 1994) Haywood, John A., dan H. M. Nahmad. “A New Arabic Grammar of the Written Language.” (Edisi kedua, 1965) Bassiouney, Reem. “Arabic Sociolinguistics: Topics in Diglossia, Gender, Identity, and Politics.” (Edisi Pertama, 2009)

FST6091102 Basic Computer Science and Programming

Module NameBasic Computer Science and Programming
Module level, if applicableUndergraduate
Module Identification CodeFST6091102
Semester(s) in which the module is taught1
Person(s) responsible for the moduleMuhaza Liebenlito, M.Si.
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursProject-Based Learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 60 min x 14 wks = 42 h  • Independent study: 3 x 60 min  x 14 wks = 42 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 124 hours
Credit points3 Credit Hours ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites
Media employedBoard, LCD Projector, Laptop/Computer
Forms of assessmentAssignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, the students should have: Capable of determining a data type and structuring programming logic, as well as implementing simple computational problems into basic programming using Python and R.  
Module content
Basic concepts of computer science and programming Branching and iteration String manipulation Array concepts in Python and R Functions in Python and R Introduction to libraries Working with files Data visualization Debugging, exceptions, and assertions Object-oriented programming concepts
Recommended Literatures John V. Guttag. Introduction to Computation and Programming Using Python 3rd Edition. MIT Press, 2021. Bill Lubanovic. Introducing Python 2nd Edition. O’Reilly, 2020. J.D. Long, Paul Teenor. R Cookbook 2nd Edition. O’Reilly, 2020. https://rc2e.com

FST6032202 Islam and Science

Module NameIslam and Science
Module level, if applicableUndergraduate
Module Identification CodeFST6032202
Semester(s) in which the module is taught1
Person(s) responsible for the moduleFardiana Fikria Qur’any, M. Ud
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursProject-based learning & problem-based learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 60 min x 14 wks = 42 h  • Independent study: 3 x 60 min  x 14 wks = 42 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 124 hours
Credit points3 Credit Hours ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites
Media employedBoard, LCD Projector, Laptop/Computer
Forms of assessmentAssignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, Students can identify and discuss the relationships between science, philosophy, and religion, understand the history of the development of science, and the integration of knowledge from classical to modern times. The study includes aspects of research and discoveries in the field of science related to themes such as human beings, technology, health, social psychology, culture, politics, economics, and so on.
Module content
Introduction to Science, Philosophy, and Religion RelationshipsHistorical Development of ScienceIntegration of Knowledge from Classical to Modern TimesResearch and Discoveries in Various Scientific ThemesScience and Human BeingsScience and TechnologyScience and HealthScience and Social PsychologyScience and CultureScience and PoliticsScience and EconomicsIntegration and Interdisciplinary AspectsCritical Analysis and DebatesFuture Trends and Implications
Recommended Literatures Abdalah, Mohammad, “The Fate of Islamic Science between the Eleventh and Sixteenth-Centuries: A Critical Study of Scholarship from Ibn Khaldun to the Present”, PhD. Dissertation, Griffith University, 2003. Ahmed, Akbar S., Postmodernisme: Bahaya dan Harapan bagi Islam, cet. IV, Terjemah, Bandung: Mizan, 1996. Ajid Thohir, Studi Kawasan Dunia Islam, Jakarta: Rajawali Press, 2009. Ancok, Djamaluddin dan Fuat Nashori Suroso, Psikologi Islami: Solusi Islam atas Problem-Problem Psikologi, Cet. IV, Yogyakarta: Pustaka Pelajar, 2001. Audi, Robert, Epistemology: A Contemporary Introduction to the Theory of Knowledge, London and New York: Routledge, 1999. Derry, Gregory N., What is Science and How It Works (United Kingdom: Princeton University Press, 1999. Franz Roshental, Knowledge Triumphant: The Concept of Knowledge in Medieval Islam (Leiden-Boston: Brill, 2007. Guessoum, Nidhal, Islam’s Quantum Question: Reconciling Muslim Tradition and Modern Science, London-New York: I.B. Tauris, 2011.  

FST6094101 Calculus I

Module NameCalculus I
Module level, if applicableUndergraduate
Module Identification CodeFST6094101
Semester(s) in which the module is taught1
Person(s) responsible for the moduleYanne Irene
LanguageIndonesian
Relation in CurriculumCompulsory course  for undergraduate program in Mathematics
Teaching methods, Contact hoursCollaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (4 x 50 min) x 14 wks = 46.67 h Structured activities: 4 h x 14 wks = 56 h Independent study: 4 h x 14 wks = 56 h  Exam:  200m x 2 (mid test and final test)  =  6.66 h Total =  165.33 h
Credit points4 Credit Hours ≈ 5.51 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesStudent should be proficient in elementary algebra
Media employedBoard, LCD Projector, Laptop/Computer
Forms of assessmentAssignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, the students should have: ability to solve problems related to some properties of real numbers  and functions. ability to solve problems on limits, continuity, derivatives, and  geometric interpretation of derivatives. ability to apply derivatives in solving problems related to limits, extreme value, and sketching a graph of a function ability to solve problems on integral and its application ability to solve problems on transcedental functions    
Module content
System of real numbers Functions and their graph The limit of a function The derivatives , the geometric  interpretation of the derivatives, higher-order derivativesExtreme value problem, applications of  extreme problem, increasing and decreasing functions, concavity, inflection points, sketching the graph of functionsDefinite IntegralApplication of IntegralTranscedental Functions  
Recommend [1] Dale Varberg, Edwin Purcell, Steve Rigdon, Calculus, 9th edition, Pearson, 2016. [2] George B. Thomas, Jr.; Maurice D. Weir, Joel R.Hass, Kalkulus Thomas Jilid 1, edisi 13, Erlangga, 2017. ed Literatures

FST6094103 Discrete Mathematics

Module NameDiscrete Mathematics
Module level, if applicableUndergraduate
Module Identification CodeFST6094103
Semester(s) in which the module is taught1
Person(s) responsible for the moduleYanne Irene
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursCollaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (4 x 50 min) x 14 wks = 46.67 h Structured activities: 4 h x 14 wks = 56 h Independent study: 4 h x 14 wks = 56 h  Exam:  200m x 2 (mid test and final test)  =  6.66 h Total =  165.33 h
Credit points4 Credit Hours ≈ 5.51 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites
Media employedBoard, LCD Projector, Laptop/Computer
Forms of assessmentAssignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, the students should have: Ability to understand mathematical reasoning in order to read, comprehend, and construct mathematical arguments. Ability to work with discrete structure, such as sets, permutation, relations. Ability to identify combinatorial problems and ability to solve using appropriate principles of combinatorics. Ability to solve problems in discrete probability. Ability to solve some linear recurrence relations Ability to prove the properties of lattice and Boolean algebra  
Module content
Logic and Proof Basic Structure: Sets, Functions, sequences, Sums, and Matrices Number Theory and Cryptography Induction and Recursion Counting Dicrete Probability Relations Boolean Algebra
Recommended Literatures Kenneth H. Rosen, (2012), Discrete Mathematics and Application to Computer Science 7th Edition, Mc-Graw Hill, USA. Rinaldi Munir, (2012), Matematika Diskrit, Bandung : Informatika..

FST6091101 Introduction to Information and Communications Technology

Module NameIntroduction to Information and Communications Technology
Module level, if applicableUndergraduate
Module Identification CodeFST6091101
Semester(s) in which the module is taught1
Person(s) responsible for the moduleMohamad Irvan Septiar Musti, M.Si
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursProject-based learning & problem-based learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (2 x 50 min) x 14 wks = 23,33 h Structured activities: 2 h x 14 wks = 28 h Independent study: 2 h x 14 wks = 28 h Exam:2 x  50 min x 2 times (mid test and final test)  = 3.33 h; Total =  82,66 h
Credit points2 Credit Hours ≈ 2.755 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites
Media employedBoard, LCD Projector, Laptop/Computer
Forms of assessmentAssignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course Students are able to understand the history, role, and benefits of Information and Communication Technology (ICT). Students are able to explain an overview of computer systems. Students are able to explain the concepts and tasks of operating systems. Students are able to explain the history of Unix, Linux, and Windows operating systems. Students can explain the definition, benefits, and workings of computer networks and the internet. Students are able to explain the processes that occur at the OSI Layer. Students are able to explain the types of IP Addresses and how they work. Students can understand the development of computing and cloud computing. Students are able to explain the architecture, storage media, and security mechanisms in cloud computing. Students have the ability to describe various types of databases and provide explanations regarding the benefits of databases. Additionally, students can identify the uses and practical applications of databases in various industries and sectors. Students have the ability to describe and understand the fundamental concepts of the Data Ecosystem, encompassing various important aspects of data management. Students have the ability to comprehensively explain programming languages. They understand the definition and purpose of programming languages and also comprehend the significant role of programming languages in software development. Students have the ability to comprehensively describe various aspects of cybercrime. They understand the definition of cybercrime, referring to illegal or harmful activities conducted online, including attacks and violations of computer systems and networks.
Module content
Pendahuluan : Sejarah Perkembangan Teknologi Informasi dan Komunikasi Sistem KomputerSistem OperasiJaringan Komputer dan Jaringan Internet Model Referensi (OSI Layer)Dasar-Dasar IP AddressSistem Cloud Computing Arsitektur, Mekanisme Keamanan dan Media Penyimpanan pada Cloud ComputingDasar Basis DataData EkosistemBahasa PemrogramanKejahatan dan Keamanan Dunia Maya
Recommended Literatures Bunrap, Pete.et al. (2019). The Cybersecurity Body of Knowledge. The National Cyber Security Center.Andrew S Tanenbaum., David J Wetherall.(2011).Computer Netwrok. 5th ed. Pearson Education.Andrew S Tanenbaum., Herbert Bos. (2015). Modern Operating System. 5th ed. Pearson Education.Andrew S Tanenbaum., Albert S Woodhull. (2006). Operating System Design and Application. 3rd ed. Pearson Education.William Stallings. (2012). Operating System Internal and Design Principles. 7th ed. Pearson Education.Huawei Technologies Co., Ltd. (2019). Cloud Computing Technology. Springer

UIN6032201 Islamic Studies

Module NameIslamic Studies
Module level, if applicableUndergraduate
Module Identification CodeUIN6032201
Semester(s) in which the module is taught1
Person(s) responsible for the moduleDr. Syamsul Aripin. MA.
LanguageIndonesian
Relation in CurriculumCompulsory course  for undergraduate program in Mathematics
Teaching methods, Contact hoursCollaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (4 x 50 min) x 14 wks = 46.67 h Structured activities: 4 h x 14 wks = 56 h Independent study: 4 h x 14 wks = 56 h  Exam:  200m x 2 (mid test and final test)  =  6.66 h Total =  165.33 h
Credit points4 Credit Hours ≈ 5.51 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesStudent should be proficient in elementary algebra
Media employedBoard, LCD Projector, Laptop/Computer
Forms of assessmentAssignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course Students are proficient in understanding the definition, origins, types, elements, and functions of religion for human life based on comprehensive, strong, rational, and convincing scriptural (naqli) and rational (‘aqli) arguments. Students are proficient in understanding the definition of Islam, its characteristics, similarities, and differences with other religions, as well as the sources and fundamental teachings of Islam based on comprehensive, strong, rational, and convincing scriptural (naqli) and rational (‘aqli) arguments. Students are proficient in understanding aspects of Islamic teachings related to worship, spiritual and moral exercises, history and culture of Islam, politics, education, preaching, community and gender equality in Islam based on comprehensive, strong, rational, and convincing scriptural (naqli) and rational (‘aqli) arguments.    
Module content
Definition, Origins, Types, Elements, Purpose, and Function of Religion.Human Needs for Religion.Islam in its True Sense.Characteristics and Principles of Islamic Teachings, Similarities and Differences with Other Religions.Essential Principles of Islam: Faith, Islam, and Ihsan/Faith, Knowledge, and Deeds.Aspects of Worship, Spiritual Exercises, and Moral Teachings in Islam.Aspects of History and Culture of Islam.Political and Institutional Aspects of Islam.Educational Aspects in Islam.Aspects of Islamic Preaching (Dakwah).Community Aspects in Islam.Aspects of Moral Development in Islam.  
Recommend Abdullah, Amin, Studi Islam Normativitas atau Historisitas, (Yogyakarta: Pustaka Pelajar, 1996). Abdullah, Taufik, Islam dan Masyarakat Pantulan Sejarah Indonesia, (Jakarta: LP3ES, 1987), cet. I. Abdullah, Yatimin, Studi Islam Kontemporer, (Jakarta: AMZSAH, 2006), cet. I. Ameer Ali,  Syeed, Api Islam (The Spirit of Islam), (Jakarta: PT Pembangunan, 1967). Azra, Azyumardi, Indonesia, Islam and Democracy: Dynamics in Global Context, (Jakarta: SOLISTICE, ICIP, The Asia Foundation, 2006). ————-, Jaringan Global dan Lokal Islam Nusantara, (Bandung: Mizan, 1423 H./2002 M.). Bahesti, Mahmud Husaini, dan Jawad Bahran, Intisari Islam, (Jakarta: Lentera, 2005); Benda, Harry J., Bulan Sabit dan Matahari Terbit-Islam Indonesia pada Masa Pendudukan Jepang, (Jakarta :Pustaka Jayam 1985), cet. II. Connoly, Peter, Aneka Pendekatan Studi Agama (The Approaches Studi of Religion), (Jakarta: LKIS, 2002), cet. I. Dirks, Jerald F., Abrahamic Faiths, Titik Temu dan Titik Seteru, (Jakarta: Serambi Ilmu Semesta, 2006). Dermenghen, Emile, Muhammad and The Islamic Tradition, (New York: The Overlook Press, 1981); Fuller, Graham E., A World Without Islam, (New York-Boston-London: Little Brown Company, tp. Th). GIBB, H.A.R., Aliran-aliran Modern dalam Islam, (Jakarta: Perdana, 1985); Grunebaum, Gustave E.Von, Islam Kesatuan dalam Keragaman, (Jakarta: Indraka, 1975).

FST6094105 Elementary Linear Algebra

Module NameElementary Linear Algebra
Module level, if applicableUndergraduate
Module Identification CodeFST6094105
Semester(s) in which the module is taught2
Person(s) responsible for the moduleDr. Gustina Elfiyanti, M.Si
LanguageIndonesian
Relation in CurriculumCompulsory course  for undergraduate program in Mathematics
Teaching methods, Contact hoursCollaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (4 x 50 min) x 14 wks = 46.67 h Structured activities: 4 h x 14 wks = 56 h Independent study: 4 h x 14 wks = 56 h  Exam:  200m x 2 (mid test and final test)  =  6.66 h Total =  165.33 h
Credit points4 Credit Hours ≈ 5.51 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesStudent should be proficient in Calculus I and Discrete Mathematics
Media employedBoard, LCD Projector, Laptop/Computer
Forms of assessmentAssignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, Students are able to solve problems (C4) related to linear equations and matrices, and present the results (A5) using both oral and written language. Students are able to solve problems (C4) related to determinants and present the results (A5) using both oral and written language. Students are able to solve problems (C4) related to Euclidean vector spaces and present the results (A5) using both oral and written language. Students are able to solve problems (C4) related to general vector spaces and present the results (A5) using both oral and written language. Students are able to solve problems (C4) related to eigenvalues and eigenvectors and present the results (A5) using both oral and written language. Students are able to solve problems (C4) related to dot products and present the results (A5) using both oral and written language. Students are able to solve problems (C4) related to diagonalization and quadratic forms, and present the results (A5) using both oral and written language. Students are able to solve problems (C4) related to linear transformations and present the results (A5) using both oral and written language.  
Module content
1. Sistem Persamaan Linier dan Matriks 2. Determinan 3. Ruang Vektor Euclid 4. Ruang Vektor Umum 5. Nilai Eigen dan Vektor Eigen 6. Hasil Kali Dalam 7. Diagonalisasi dan Bentuk Kuadratik 8. Transformasi Linier  
Recommend Howard Anton, Elementary Linear Algebra, Application Version, 11th ed., John Wiley & Sons, Inc , 2013Wono Setya Budhi, Aljabar Linear, PT. Gramedia Pustaka Utama, 1995Gustina Elfiyanti, Modul Perkuliahan Aljabar Linier Elementer, tidak diterbitkan.

NAS6013203 Indonesian

Module NameIndonesian
Module level, if applicableUndergraduate
Module Identification CodeNAS6013203
Semester(s) in which the module is taught2
Person(s) responsible for the moduleDidah Nurhamidah, M.Pd
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursProject-based learning & problem-based learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 60 min x 14 wks = 42 h  • Independent study: 3 x 60 min  x 14 wks = 42 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 124 hours
Credit points3 Credit Hours ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites
Media employedBoard, LCD Projector, Laptop/Computer
Forms of assessmentAssignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
Speaking Skills in Academic Presentation: Students are able to speak in scientific presentations.Understanding the Development of the Indonesian Language: Students can understand the development of the Indonesian language.Understanding the Use of Letters and Words: Students can understand the use of letters and words.Understanding Borrowed Words and Punctuation: Students can understand borrowed words and punctuation.Proper Diction Usage: Students are able to use appropriate diction.Crafting Effective Sentences: Students are able to create effective sentences.Constructing Proper Paragraphs: Students are able to create proper paragraphs.Understanding Plagiarism: Students understand plagiarism.Essay Planning Abilities: Students are able to plan an essay.Effective Reasoning Skills: Students are able to reason accurately.Utilizing Scientific Notation Efficiently: Students are able to use scientific notation efficiently.Producing Short Writings Correctly: Students are able to produce short writings correctly.Reproduction of Writing Accurately: Students are able to reproduce writings accurately.
Module content
Speaking in Scientific Presentations;Development of the Indonesian Language;Usage of Letters and Words;Borrowed Elements, Punctuation, and Transliteration;Diction/Word Choice;Effective Sentences;Paragraphs;Scientific Ethics/Plagiarism;Essay Planning;Reasoning;Scientific Notation;Short Writing Production;Writing Reproduction.
Recommended Literatures Arifin, E. Zainal dan S. Amran Tasai. Cermat Berbahasa Indonesia.Jakarta : Akademika Pressido, 2006Akhadiah, Sabarti dan Sakura Ridwan. Pembinaan Kemampuan Menulisbahasa Indonesia. Jakarta : Airlangga, 1993Finoza, Lamuddin. Komposisi Bahasa Indonesia. Jakarta : Diksi InsanMulia, 2001.Gani, Ramlan A dan Mahmudah Fitriyah Z.A. Disiplin BerbahasaIndonesia. Jakarta : PTIK Press, 2010.Hs., Widjono. Bahasa Indonesia. Jakarta : Grasindo, 2007.Keraf, Gorys. Komposisi. Ende : Nusa Indah, 1993.Putra, R. Masri Sareb Putra. Kiat Menghindari Plagiat. How to AvoidPlagiarisme. Jakarta : Indeks, 2011.  

UIN6014203 English

Module NameEnglish
Module level, if applicableUndergraduate
Module Identification CodeUIN6014203
Semester(s) in which the module is taught2
Person(s) responsible for the moduleChilda Faiza M.Pd
LanguageIndonesian and English
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursCollaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 60 min x 14 wks = 42 h  • Independent study: 3 x 60 min  x 14 wks = 42 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 124 hours
Credit points3 Credit Hours ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites
Media employedBoard, LCD Projector, Laptop/Computer
Forms of assessmentAssignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, the students should have: Able to implement reading strategies such as “skimming” and “scanning”, identifying pronoun references, using punctuation correctly, recalling oral information, and introducing oneself. Understanding the main ideas and supporting ideas in a reading, using “verbs” and “adverbs” using “mind mapping”, and discussing daily activities. Knowing the difference between facts and opinions in a reading, using adjectives appropriately, understanding simple opinions, and being able to describe someone. Identifying important information from the reading text, writing simple sentences, being able to ask and answer about directions. Able to draw conclusions from the reading text, understanding the use of pronouns and articles, writing a memo, making/receiving/declining meeting appointments. Paraphrasing sentences from the reading text, using the “simple present tense”, writing a postcard, expressing likes or dislikes. Identifying the meanings of words or phrases in the reading text, making conclusions, using the “simple future tense” appropriately, writing simple advertisements, verbally inviting. Identifying the purpose of writing in a reading text, using the “simple past tense” correctly, writing personal information.
Module content
Mastering Effective Reading Strategies Comprehension and Language Proficiency Information Extraction and Language Expression Skills Language Transformation and Expressing Preferences Enhancing Vocabulary and Future Expressions Understanding Writing Purpose and Past Expressions
Recommended Literatures Azkiyah, Siti Nurul et al.( 2020). General English 1 (A course for University Students). Malaysia: Oxford University Press. Azar, B.S. (1999). Understanding and using English Grammar (3rded). New York: Pearson Education. Cusack, B., & McCarter, S. (2007). Listening and Speaking skills. Oxford: MacMillan Publisher Limited Hewings, M. (2002). Advance Grammar in use: A self Study. Cambridge: Cambridge University Press.  

FST6094104 Calculus II

Module NameCalculus II
Module level, if applicableUndergraduate
Module Identification CodeFST 6094104
Semester(s) in which the module is taught2
Person(s) responsible for the moduleMahmudi, M.Si Dr. Suma Inna, M.Si
Language Indonesian
Relation in CurriculumCompulsory course 
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
WorkloadLecture (class): (4 x 50 min) x 14 wks = 46.67 h Structured activities: 4 h x 14 wks = 56 h Independent study: 4 h x 14 wks = 56 h  Exam:  200m x 2 (mid test and final test)  =  6.66 h Total =  165.33 h
Credit points4 Credit Hours ≈ 5.51 ECTS
Admission and examination requirementsEnrolled in this course • Minimum  80% attendance in lecture
Recommended prerequisitesCalculus I
Media employeda whiteboard and projector
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Students able to solve (C4) problems related to techniques of integration, indeterminate forms, improper integrals, infinite series, conics, and polar coordinates,  and be able to present (A5) the results
Module content
Lecture (Class Work)   Techniques of integrationIndeterminate formsImproper integralsInfinite seriesConics and polar coordinates
Recommended Literature Dale Varberg, Edwin Purcell, and Steve Rigdon, Calculus, 9rd edition, Prentice Hall, New Jersey, 2009Koko Martono.,   Kalkulus, Erlangga, Jakarta, 1999Howard Anton,  Calculus, third edition, Jhon Wiley dan Sons, New York, 1988  

NAS6112201 Pancasila and Civic Education

Module NamePancasila and Civic Education
Module level, if applicableUndergraduate
Module Identification CodeNAS6112201
Semester(s) in which the module is taught2
Person(s) responsible for the moduleDr. Gerafina Djohan, MA
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursProject-based learning & problem-based learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 60 min x 14 wks = 42 h  • Independent study: 3 x 60 min  x 14 wks = 42 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 124 hours
Credit points3 Credit Hours ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites
Media employedBoard, LCD Projector, Laptop/Computer
Forms of assessmentAssignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, the students should have: Explaining the History of the Formulation of Pancasila Stressing the Importance of Civic Education as a Platform for Shaping the Character of the Civilized Indonesian Nation Describing the Competency Standards of Civic Education Presenting the Scope of Pancasila and Civic Education Material Concluding the Importance of Civic Education for the Development of a Democratic Culture in Indonesia
Module content
History of the Formulation of Pancasila Pancasila as a National Ideology Pancasila as a Paradigm for Community, Nation, and State Life Islamic Perspectives on the Content of Pancasila National Identity Globalization Democracy Constitution and Legislation in Indonesia State, Religion, and Citizenship Human Rights (HAM) Regional Autonomy Good Governance Corruption Prevention Civil Society
Recommended Literatures Ubaedillah, A. 2015. Pendidikan       Kewarganegaraan. Jakarta: Prenada Media Group. Endang Saefudin Anshari. 1985. Piagam  Jakarta. Pustaka, bandung    

UIN6033205 Practicum Qira’ah and Worship

Module NamePracticum Qira’ah and Worship
Module level, if applicableUndergraduate
Module Identification CodeUIN6033205
Semester(s) in which the module is taught2
Person(s) responsible for the moduleDr. Syamsul Aripin M.A.
LanguageIndonesian and Arabic
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursPracticum,Collaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
Workload• Lecture (practicum): (2 x 150 min) x 14 wks = 70 h • Structured activities: 2 x 150 min x 14 wks = 70 h • Exam: lecture 2 h x 2 times = 4 h; • Total = 144  hours
Credit points2 Credit Hours ≈ 4.8 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites
Media employedBoard, LCD Projector, Laptop/Computer
Forms of assessmentAssignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course Students are able to master the theory of “tilawah” or the recitation, including the correct pronunciation of each Arabic letter based on its articulation and characteristics.Students are able to understand the theory of “tajwid” (rules of Quranic recitation) in the reading of the Quran and “gharib al-Quran” accurately and appropriately.Memorize short chapters and selected chapters of the Quran.Students comprehend the theory of both obligatory (“mahdlah”) and non-obligatory (“ghairu mahdlah”) worship through practical application.Capable of applying the correct pronunciation of Arabic letters with fluency.Capable of applying the knowledge of Tajwid (rules of Quranic recitation) in reading the Quran.Proficient in practicing both obligatory (“Mahdlah”) and non-obligatory (“ghairu mahdlah”) worship correctly and appropriately.
Module content
Theory of Tilawah (Recitation):Theory of Tajwid (Rules of Quranic Recitation):Quranic Memorization:Understanding Worship Theory:Application of Correct Pronunciation:Application of Tajwid Knowledge:Proficient Worship Practice:  
Recommended Literatures Practical Module on Qiraah (Quranic Recitation) and Worship

FST6094107 Practicum Elementary Statistics

Module NamePracticum Elementary Statistics
Module level, if applicableUndergraduate
Module Identification CodeFST6094107
Semester(s) in which the module is taught2
Person(s) responsible for the moduleDr. Nina Fitriyati, M.Kom
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursPracticum, Collaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
Workload• Lecture (practicum): (1 x 150 min) x 14 wks = 35 h • Structured activities: 150 min x 14 wks = 35 h • Exam: lecture 2 h x 2 times = 4 h; • Total = 74  hours
Credit points2 Credit Hours ≈ 2,446 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites
Media employedBoard, LCD Projector, Laptop/Computer
Forms of assessmentAssignments (including quizzes and assignment): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
After completing this course, the students should have: Able to understand (C2) various statistical software. Proficient in using (C3) statistical software and interpreting its output related to data visualization in the form of stem-and-leaf plots, histograms, dot plots, and box plots; outlier detection; concepts of measures of central tendency, measures of variability, descriptive statistics, and outliers. Proficient in using (C3) statistical software and interpreting its output related to data transformation. Proficient in using (C3) statistical software and interpreting its output related to normal distribution, sampling distribution, and central limit theorem. Proficient in using (C3) statistical software and interpreting its output related to chi-square distribution, t-distribution, and F-distribution probability. Proficient in using (C3) statistical software and interpreting its output related to estimation of population parameters. Proficient in using (C3) statistical software and interpreting its output related to statistical inference. Proficient in using (C3) statistical software and interpreting its output related to correlation coefficients and linear regression. Proficient in using (C3) statistical software and interpreting its output related to non-linear regression.
Module content
Introduction to Software UsedData Visualization and Data TransformationNormal DistributionSampling Distribution and Central Limit TheoremChi-square, t, and F DistributionsEstimation of Population ParametersStatistical InferenceCorrelation Coefficients and InferenceSimple Linear Regression AnalysisMultiple Linear Regression AnalysisNonlinear Regression Analysis  
Recommended Literatures R. E. Walpole & R. H. Myers, Probability Statistics for Engineers and Scientists, 7th edition, 2002, Prentice Hall International Edition.  

FST6094106 Elementary Statistics

Module NameElementary Statistics
Module level, if applicableUndergraduate
Module Identification CodeFST6094106
Semester(s) in which the module is taught2
Person(s) responsible for the moduleDr. Nina Fitriyati,M.Kom.
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesDiscrete Mathematics
Media employedBoard, LCD Projector, Laptop/Computer
Forms of assessmentMidterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
Able to understand (C2) types of data, population, sample, and organize and summarize data using graphs and tables. Able to comprehend (C2) concepts of measures of central tendency, measures of variability, descriptive statistics, and outliers. Able to calculate (C3) probabilities, conditional probabilities, and Bayes’ theorem based on probability concepts (sets, sample space, permutation, combination). Able to apply (C3) normal distribution, sampling distribution, and the central limit theorem to everyday life problems. Able to understand (C2) chi-square distribution, t-distribution, and F-distribution. Able to calculate/estimate (C3) population parameter intervals based on statistical inference concepts. Able to decide (C5) acceptance/rejection of statistical hypotheses for one and two populations based on test statistic values and p-values. Able to determine (C5) significant regression coefficient estimators and correlations. Able to determine (C5) significant regression models based on one-way analysis of variance.
Module content
Lecture (Class Work) Introduction to Statistics Organizing and Summarizing Data in Graphs and Tables Statistical Measures for Data Probability Random Variable Distributions Discrete Probability Normal Distribution Sampling Theory Parameter Estimation Hypothesis Testing Regression Correlation One-Way Analysis of Variance
Recommended Literatures R. E. Walpole & R. H. Myers, Probability Statistics for Engineers and Scientists, 7th edition, 2002, Prentice Hall International Edition.s

FST6094113 Exploration Data Analysis

Module NameExploration Data Analysis
Module level, if applicableUndergraduate
Module Identification CodeFST 6094113
Semester(s) in which the module is taught3
Person(s) responsible for the moduleDr. Taufik Sutanto
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 60 min x 14 wks = 42 h  • Independent study: 3 x 60 min  x 14 wks = 42 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesElementary StatisticsBasic Programming
Media employedLMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessmentMidterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
students are able to identify information and insights hidden in data using various statistical and machine learning methods and provide recommendations that can be utilized by users.
Module content
Lecture (Class Work) Introduction of Exploratory Data AnalysisReview Python for Data AnalysisDigital Data Gathering & Data Transformation.Data Wrangling and Basic Statistics for EDA.Outlier and missing values.Anomaly and outlier detection.Visualization on structured dataVisualization on Time-Series DataData Story Telling
Recommended Literatures McKinney, W., 2012. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. ” O’Reilly Media, Inc.”.Iliinsky, N., & Steele, J. (2011). Designing data visualizations: Representing informational Relationships. ” O’Reilly Media, Inc.”.Dykes, B. (2019). Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals. John Wiley & Sons, Incorporated.Cox, V. (2017). Exploratory data analysis. In Translating Statistics to Make Decisions (pp. 47-74). Apress, Berkeley, CA.Healy, K., 2018. Data Visualization: A Practical Introduction. Princeton University Press.Knaflic, C.N., 2015. Storytelling with Data. WileyRobbins, N.B., 2005. Creating More Effective Graphs. Wiley.Tufte, E.R., 2001. The Visual Display of Quantitative Information, 2nd ed. Cheshire, CT: Graphics Press

FST6094108 Multivariable Calculus

Module NameMultivariable Calculus
Module level, if applicableUndergraduate
Module Identification CodeFST 6094108
Semester(s) in which the module is taught3
Person(s) responsible for the moduleDr. Suma Inna, M.Si Mahmudi, M.Si
Language Indonesian
Relation in CurriculumCompulsory course 
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
WorkloadLecture (class): (4 x 50 min) x 14 wks = 46.67 h Structured activities: 4 h x 14 wks = 56 h Independent study: 4 h x 14 wks = 56 h  Exam:  200m x 2 (mid test and final test)  =  6.66 h Total =  165.33 h
Credit points4 Credit Hours ≈ 5.51 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisitesCalculus II
Media employeda whiteboard and and projector
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
After completing the course, students have the ability Capable of explaining (C1) (A5) the concepts of parametric and scalar functions both verbally and in writing. Able to solve problems (C4) related to partial derivatives of scalar functions and present the results logically and systematically both verbally and in writing. Capable of solving problems (C4) related to vector functions and presenting the results logically and systematically both verbally and in writing. Able to solve problems (C4) related to double and triple integrals, line integrals, and surface integrals of multivariable functions and present them clearly both verbally and in writing. Able to demonstrate (C4) the relationship between multiple integrals and line integrals both verbally and in writing.


Module content
Lecture (Class Work) Parametric Functions: Limits and continuity of parametric functions; integrals and arc length of parametric functions.Scalar Functions: Height curves; limits and continuity; partial derivatives and gradient vectors; differentiability; total differentials; chain rule; directional derivatives; implicit differentiation; function extrema; Lagrange method.Vector Functions: Divergence and curl, conservative vector fields; chain rule; Jacobian matrices; inverse vector functions.Multiple Integrals: Double integrals; triple integrals; coordinate transformation for multiple integrals (polar, curvilinear, cylindrical, and spherical coordinates).Line Integrals: Line integrals of vector fields; connection between line integrals and multiple integrals (Green’s theorem, Stokes’ theorem, fundamental theorem of line integrals, Gauss’s divergence theorem).Surface Integrals: Connection between surface integrals and multiple integrals (Stokes’ theorem in space, Gauss’s divergence theorem in space).  
Recommended Literature James Stewart, Multivariable Calculus Seventh Edition, McMASTER UNIVERSITY AND UNIVERSITY OF TORONTOWono Setya Budhi. Kalkulus peubah banyak dan penggunaannya. ITB Bandung. Bandung

FST6094112 Numerical Method

Module NameMetode Numerik (Numerical Methods)
Module level, if applicableUndergraduate
Module Identification CodeFST6094112
Semester(s) in which the module is taught4
Person(s) responsible for the moduleMuhaza Liebenlito
LanguageIndonesian
Relation in CurriculumElective course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (4 x 50 min) x 14 wks = 46.67 h Structured activities: 4 h x 14 wks = 56 h Independent study: 4 h x 14 wks = 56 h  Exam:  200m x 2 (mid test and final test)  =  6.66 h Total =  165.33 h
Credit points4 Credit Hours ≈ 5.51 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesCalculus I and IIElementary Linear AlgebraBasic Programming
Media employedLMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessmentMidterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
Students are able to apply and choose various numerical methods for solving basic mathematical problems e.g. root finding, interpolation, numerical integration, and matrix decomposition using Python
Module content
Lecture (Class Work) Error analysis in numerical solution Root finding problems Numerical solution of linear equations Matrix decomposition Interpolation Numerical derivative and integration Eigenvalue and eigenvectors
Recommended Literatures Burden, Faires. Numerical Analysis 9th Edition. Cengage Learning, 2011. Stephen C. Chapra. Numerical Methods for Engineers 7th Edition. McGraw-Hill Education. 2015.

FST6094110 Introduction to Real Analysis

Module NameIntroduction to Real Analysis I
Module level, if applicableUndergraduate
Module Identification CodeFST 6094110
Semester(s) in which the module is taught3
Person(s) responsible for the moduleDr. Gustina Elfiyanti, M.Si
Language Indonesian
Relation in CurriculumCompulsory course  for undergraduate program in Mathematics
Teaching methods, Contact hoursCollaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (4 x 50 min) x 14 wks = 46.67 h Structured activities: 4 h x 14 wks = 56 h Independent study: 4 h x 14 wks = 56 h  Exam:  200m x 2 (mid test and final test)  =  6.66 h Total =  165.33 h
Credit points4 Credit Hours ≈ 5.51 ECTS
Admission and examination requirementsEnrolled in this course • Minimum  80% attendance in lecture
Recommended prerequisitesDiscrete Mathematics, Calculus I
Media employeda whiteboard and projector
Forms of assessmentMidterm exam 20%, Final exam 30%, Quiz 20%, Structured assignment 20%, Activeness 10%
Intended Learning Outcome
Students Able to solve problems (C4) related to properties of real numbers, sequences and seried of real numbers, real valued functions, limit and continuity of function as well express the results (A5) in spoken and written language  
Module content
Lecture (Class Work) 1. Set Theory and functions. 2. Real numbers. 3. Sequences and series of real numbers. 4. Real valued functions. 5. Limits and continuity of function.  
Recommended Literature R.G. Bartle and D.R. Sherbert (2000) , Introduction to Real Analysis, Edis 4. John Wiley & Sons, New YorkF. Morgan, Real Analysis and Applications, AMS, 2005.

FST6094111 Introduction to Financial Mathematics

Module NameIntroduction to Financial Mathematics (Pengantar Matematika Keuangan)
Module level, if applicableUndergraduate
Module Identification CodeFST6094111
Semester(s) in which the module is taught3
Person(s) responsible for the moduleNina Fitriyati
LanguageIndonesian
Relation in CurriculumMain course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussions. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h  • Structured activities: 3 x 60 min x 14 wks = 42 h  • Independent study: 3 x 60 min  x 14 wks = 42 h  • Exam:  3 x 50 min x 2 times = 5 h;  • Total = 124 hours
Credit points3 Credit Hours ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesElementary Statistics and Multivariable Calculus
Media employedClassical teaching tools with whiteboard, PowerPoint presentation, and practices in computer class
Forms of assessmentAssignments (including quizzes and group projects): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students will be able to calculate the present value and accumulation value of money and annuities based on the effective and nominal interest rates and apply the basic theory of interest to the payment method of loan, calculate the yield rates, and calculate the obligation and stock prices, calculate the spot and forward rates.
Module content
Interest rate Basic annuities More general annuities Amortization and sinking funds Yield rates Obligation and stock Spot and forward rates Inflation Duration
Recommended Literatures Kellison, S.G., 2009, Theory of Interest (3rd Edition), McGraw-Hill Education.

FST6094109 Mathematical Statistics

Module NameMathematical Statistics I (Statistika Matematika I )
Module level, if applicableUndergraduate
Module Identification CodeFST6094109
Semester(s) in which the module is taught3
Person(s) responsible for the moduleNina Fitriyati
LanguageIndonesian
Relation in CurriculumMain course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussions. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
WorkloadLecture (class): (4 x 50 min) x 14 wks = 46.67 h Structured activities: 4 h x 14 wks = 56 h Independent study: 4 h x 14 wks = 56 h  Exam:  200m x 2 (mid test and final test)  =  6.66 h Total =  165.33 h
Credit points4 Credit Hours ≈ 5.51 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesElementary Statistics and Multivariable Calculus
Media employedClassical teaching tools with whiteboard, PowerPoint presentation, and practices in computer class
Forms of assessmentAssignments (including quizzes and group projects): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students will be able to determine the distribution function of the random variable(s) and function of the random variable(s), determine the moment, apply some special distributions of discrete and continuous random variables to real-life problems, and determine sampling distribution.
Module content
Lecture (Class Work) Sample space and probability. Probability density function, expectation, and moment generating function for a single random variable. Transformation of the function of a single random variable. Joint probability density function, expectation, and moment generating function for multiple random variables. Transformation of the function of multivariate random variables. Special distributions of discrete random variables. Special distributions of continuous random variables. Beta, Student, and Fisher distribution. Sampling distribution.
Recommended Literatures 1. Hogg, R.V., McKean, J. W., and Craig, A.T., Introduction to Mathematical Statistics, 7th Edition, 2013.Rohatgi, V. K., Statistical Inference, John Wiley & Sons, Inc., New York, 1984.

FST6091304 Algorithm and Data Structure

Module NameAlgoritma dan Struktur Data (Algorithms and Data Structures)
Module level, if applicableUndergraduate
Module Identification CodeFST6091304
Semester(s) in which the module is taught4
Person(s) responsible for the moduleMuhaza Liebenlito
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (4 x 50 min) x 14 wks = 46.67 h Structured activities: 4 h x 14 wks = 56 h Independent study: 4 h x 14 wks = 56 h  Exam:  200m x 2 (mid test and final test)  =  6.66 h Total =  165.33 h
Credit points4 Credit Hours ≈ 5.51 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesCalculus IDiscrete MathematicsBasic Programming
Media employedLMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessmentMidterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
students are able to analyse the complexity of various algorithms and be able to choose an appropriate data structure to implement an algorithm using programming languange.
Module content
Lecture (Class Work) Basic concept of algorithms and data structuresGrowth of function and asymptotic notations Algorithm design techniquesData structures Graph algorithms
Recommended Literatures Cormen, et. al. 2009. Introduction to Algorithms Third Edition. MIT Press.Goodrich, et. al. 2013.  Data Structures and Algorithms in Python. John Wiley & Sons, Inc.

FST6094120 Geometry

Module NameGeometry
Module level, if applicableUndergraduate
Module Identification CodeFST6094120
Semester(s) in which the module is taught4
Person(s) responsible for the moduleYanne Irene
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursCollaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
Workload.Lecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesCalculus 1 and Elementary Linear Algebra
Media employedBoard, LCD Projector, Laptop/Computer
Forms of assessmentAssignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The course is intended to provide a good basic knowledge and training on analytic geometry to students via vectors approach. After completing this course the students should have : ability to solve problems on geometry in two-dimensional space through its equations such as straight lines, conic sections, equations in polar coordinates, and parametric equations. ability to use translation and rotation to simplify and sketch the graph of the second-degree equations in two- dimensional space. ability to solve problems on geometry in three-dimensional spaces through its equations such as straight lines and planes. ability to sketch second-degree equations in three- dimensional space, such as cylinders, paraboloids, ellipsoids, hyperboloids, and cones.
Module content
Vectors in ℝ2 and ℝ3. Equations of straight lines in two- dimensional space : parallel lines and two perpendicular lines, angle between two lines, distance between a point and a line, lines in polar coordinates. Second-degree equations in ℝ2 : circles, parabolas, ellipses, hyperbolas, conic section in polar coordinates. Curves in polar coordinates.  Parametric equations: writing Cartesian equations in parametric form, parametric equations of circles, cycloids, hypocycloids, epicycloids, and asteroids. Transformation coordinates: translation and rotation of axes. Straight lines and planes in three- dimensional space. Second-degree equations in three-dimensional space: cylinders, spheres, ellipsoids, paraboloids, hyperboloids, hyperbolic paraboloids, cones. Cylindrical and spherical coordinates.
Recommended Literature 1. Thomas, George B., 2014, Calculus Thirteenth Edition. United States of America: Pearson Education, Inc.  

 

FST6094119 Linear Models

Module NameLinear Model
Module level, if applicableUndergraduate
Module Identification CodeFST6094119
Semester(s) in which the module is taught4
Person(s) responsible for the moduleAry Santoso
LanguageIndonesian
Relation in CurriculumMain course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesElementary Statistics and Elementary Linear Algebra
Media employedClassical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessment·        Assignments (including quizzes and group project): 40% ·        Midterm exam: 30% ·        Final exam: 30%
Intended Learning Outcome
Estimate regression coefficient using matrix Explain the basis for developing the best linear regression model Analyze a real data set and interpret the output of statistical software in correct way
Module content
The teaching materials consist of Simple Linear Regression and Correlation, Model Adequacy Checking. Multiple Linear Regression, Indicator Variables, Variable Selection and Model Building.
Lecture (Class Work) Simple Linear Regression and Correlation Model Adequacy Checking. Multiple Linear Regression, Indicator Variables Variable Selection Model Building.
Recommended Literature Weisberg, S. 1980. Applied Linear Regression. New York. John Wiley & Sons.Witting, D. R. 1988. The Application of Regression Analysis. Allyn and Bacon, Inc, Massachusetts 3.   Juanda, Bambang. 2009. Ekonometrika Pemodelan dan Pendugaan. Bogor. Institut Pertanian Bogor Press. 4.   Fox, John and Sanford Weisberg. 2019. An R Companion to Applied Regression 3rd Edition. London. SAGE Publication, Inc. 

FST6094114 Introduction to Real Analysis II

Module NameIntroduction to Real Analysis II
Module level, if applicableUndergraduate
Module Identification CodeFST 6094114
Semester(s) in which the module is taught4
Person(s) responsible for the moduleDr. Suma Inna, M.Si Dr. Gustina Elfiyanti, M.Si
Language Indonesian
Relation in CurriculumCompulsory course 
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
WorkloadLecture (class): (4 x 50 min) x 14 wks = 46.67 h Structured activities: 4 h x 14 wks = 56 h Independent study: 4 h x 14 wks = 56 h  Exam:  200m x 2 (mid test and final test)  =  6.66 h Total =  165.33 h
Credit points4 Credit Hours ≈ 5.51 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisitesIntroduction to Real Analysis I
Media employeda whiteboard and and projector
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
After completing the course, students have the ability Able to prove (C5) the limit and continuity of a function.Able to prove (C5) derivatives and properties related to derivatives and apply them to the Roles Theorem, Mean Value Theorem, and Taylor’s Theorem.Able to prove (C5) the integrability of continuous functions and monotonic functions.Able to prove (C5) the convergence of sequences and series of real-valued functions and their properties.  
Module content
Lecture (Class Work) Continuous Function Derivative. Riemann Integral . Sequences and Series of Functions.
Recommended Literature   1.     R.G. Bartle and D.R. Sherbert (2000) , Introduction to Real Analysis, John Wiley & Sons, New York W. Rudin, Principles of Mathematical Analysis, 3rd ed., McGraw-Hill, 1989.T.M. Apostol, Mathematical Analysis, 2nd ed., Addison Wesley, 1978.Other books on Real Analysis or Advanced Calculus

FST6094115 Introduction to Stochastic Process

Module NameIntroduction to Stochastic Processes
Module level, if applicableUndergraduate
Module Identification CodeFST6094115
Semester(s) in which the module is taught4
Person(s) responsible for the moduleMadona Yunita Wijaya
LanguageIndonesian
Relation in CurriculumElective course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesElementary Statistics and Mathematical Statistics I
Media employedClassical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessmentAssignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students will have the ability to apply the probability concept and random variable to understand and successfully use of stochastic processes and their application. The students will also have the ability to analyze simple real-world phenomenon that can be modeled through basic classes of stochastic process such as Markov Chain and Poisson process.
Module content
Lecture (Class Work) Introduction to probability theory Random variables, distribution, expectation Discrete Markov Chain IntroductionChapman-Kolmogorov equationsClassification of statesLimiting probabilitiesAbsorbing state Poisson Process IntroductionExponential distributionCounting process and Poisson processInterarrival and waiting timeSplitting and merging of Poisson processes Continuous Markov Chain  
Recommended Literatures 1.    Ross. (2010). Introduction to Probability Models, 10th edition. John Wiley and Sons. 2.    Taylor & Karlin. (1998). An Introduction to Stochastic Modeling.

FST6094117 Ordinary Differential Equations

Module NameOrdinary Differential Equations
Module level, if applicableUndergraduate
Module Identification CodeFST 6094117
Semester(s) in which the module is taught4
Person(s) responsible for the moduleMuhammad Manaqib, M.Sc. Irma Fauziah, M.Sc.
Language Indonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursCollaborative learning & discussion-based learning, structured activities (homework, quizzes, case-based/project-based assignments).
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesCalculus 2 Elementary Linear Algebra
Media employedClassical teaching tools with white board and PowerPoint presentation
Forms of assessmentMidterm exam 30%, Final exam 40%, Quiz 10%, Structured assignment 20%
Intended Learning Outcome
Students are expected to be able to solve problems related to the concepts of ordinary differential equations and find solutions to ordinary differential equations.  
Module content
Lecture (Class Work) Definition of ODE, PDE, linear and nonlinear ODE, and the order of ODE First order differential equations: separable equations, homogeneous equations, exact equations and integrating factors, linear equations, Bernoulli’s differential equation. Higher order linear differential equations: Reduction of order, nonhomogeneous differential equationsand their method of solutions(the method of undetermined coefficients, variation of parameters), reduction of order, Cauchy-Euler equations. Solution of second order of ODE using infinite series Laplace transform and its application to solve ODE System of linear and nonlinear of an ODE
Recommended Literatures Shepley l. Ross. Introduction to Ordinary Differential Equations, 4ed, John Wiley & Sons. Boyce, WE, Di Prima, RC. Elementary Differential Equations and Boundary Value Problems. 6ed, John Wiley & Sons. Schaum Series, Differential Equations, McGraw Hill, 1975

FST6094118 Mathematical Statistics II

Module NameMathematical Statistics II (Statistika Matematika II )
Module level, if applicableUndergraduate
Module Identification CodeFST6094118
Semester(s) in which the module is taught4
Person(s) responsible for the moduleNina Fitriyati
LanguageIndonesian
Relation in CurriculumMain course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussions. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesElementary Statistics and Multivariable Calculus
Media employedClassical teaching tools with whiteboard, PowerPoint presentation, and practices in computer class
Forms of assessmentAssignments (including quizzes and group projects): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students will be able to solve problems related to the basics of statistical inference, deduce distribution limits from random variable sequences, re-prove the central limit theorem and other important theorems, deduce estimators from population parameters, and validate sufficinet statistics from these parameters.
Module content
Lecture (Class Work) Sampling and statistics Confidence intervals Order statistics Hypothesis testing Convergence in probability Convergence in distribution Moment generating function method Central Limit Theorem and other theorems Maximum Likelihood Method Monte Carlo Method and Bootstrap procedure Sufficient Statistics
Recommended Literatures Hogg, R.V., McKean, J. W., and Craig, A.T., Introduction to Mathematical Statistics, 7th Edition, 2013.Rohatgi, V. K., Statistical Inference, John Wiley & Sons, Inc., New York, 1984.

FST6092035 Technopreneur

Module NameTechnopreneur
Module level, if applicableUndergraduate
Module Identification CodeFST 6092035
Semester(s) in which the module is taught5
Person(s) responsible for the moduleDr. Nur Inayah, M. Si / Dr.Taufik Edy Sutanto, MSc.Tech
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hours  The course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (2 x 50 min) x 14 wks = 23,33 h Structured activities: 2 h x 14 wks = 28 h Independent study: 2 h x 14 wks = 28 h Exam:2 x  50 min x 2 times (mid test and final test)  = 3.33 h; Total =  82,66 h
Credit points82,66 / 30 =  2.755 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisites
Media employedClassical teaching tools with white board and Power Point presentation
Forms of assessmentMidterm exam 40%, Final exam 40%, Quiz 10%, Structured assignment 10%
Intended Learning Outcome
  After completing the course, the Students will have the ability to develop an entrepreneurial spirit and analyze entrepreneurial activities.
Module content
Lecture (Class Work) Technopreneurship and InspirationMotivation and Technopreneurship OpportunitiesTechnopreneurship Organizational GovernanceTechnopreneurship OwnershipEthical Considerations in TechnopreneurshipTechnopreneurship IntelligenceCapital and Financial ManagementProduct DesignForms of MarketingEnvironmental AnalysisCompetitor AnalysisMonitoring and EvaluationTechnopreneurship RevolutionBusiness Plan  
Recommended Literatures Inayah, Nur, Achmad Tjachja, and Moh. Irvan, 2021, Introduction to Entrepreneurship, Andi Publisher, Yogyakarta.Rusman Hakim, Success Tips for Entrepreneurship, Gramedia, Jakarta, 2009.Masykur Wiratno, Introduction to Entrepreneurship: Basic Framework for Entering the Business World, BPFE, Yogyakarta, 2010.Peter F. Drucker, Innovation and Entrepreneurship: Practice and Fundamentals, Gelora Aksara Pratama, 2012.H. Fatkul Muin, Let’s Be Entrepreneurs, 2014.Darmanto, Entrepreneurship, 2017.Edy Dwi Kurniati, Industrial Entrepreneurship, 2017.Dyanasari and Asnah, Small Business Management and Entrepreneurship, 2018.Ika Sari Dewi, S.S., M.Si., and I.K. Sihombing, M.Si., Entrepreneurship and Strategic Management of Rural SMEs, 2019.Muh. Saleh Malawat, Entrepreneurship in Education, 2019.Nathanael Sitanggang and Putri Lynna A. Luthan, Entrepreneurship Management in the Furniture Industry, 2019.Rachmat Hidayat, SKM., M.Kes, Cultivating Entrepreneurial Spirit, 2019.Prof. Dr. H. Saban Fchdar, S.E., M.Si, Dr. Maryadi, S.E., M.M, Business Ethics and Entrepreneurship, 2019.  

FST6094104 Complex Function

Module NameComplex Function
Module level, if applicableUndergraduate
Module Identification CodeFST 6094122
Semester(s) in which the module is taught5
Person(s) responsible for the moduleDhea Urfina Zulkifli, M.Si. Dr. Suma Inna, M.Si
Language Indonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum  80% attendance in lecture
Recommended prerequisitesCalculus I
Media employeda whiteboard and projector
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Students able to solve (C4) problems related to complex numbers, complex functions, complex integrals, complex series, and residues and their uses, and be able to present (A5) the results
Module content
Lecture (Class Work) Complex numbersComplex functionsComplex integralsComplex seriesResidues and their uses
Recommended Literature James Ward Brown & Ruel V. Churchill, Complex Variables and Applications, 8th ed., 2009, McGraw-Hill, Inc., International Editions.

FST6094121 Introduction to Algebra Abstract

Module NameIntroduction to Abstract Algebra
Module level, if applicableUndergraduate
Module Identification CodeFST 6094121
Semester(s) in which the module is taught5
Person(s) responsible for the moduleDr. Gustina Elfiyanti, M.Si
Language Indonesian
Relation in CurriculumCompulsory course  for undergraduate program in Mathematics
Teaching methods, Contact hoursProject-based learning & problem-based learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (4 x 50 min) x 14 wks = 46.67 h Structured activities: 4 h x 14 wks = 56 h Independent study: 4 h x 14 wks = 56 h  Exam:  200m x 2 (mid test and final test)  =  6.66 h Total =  165.33 h
Credit points4 Credit Hours ≈ 5.51 ECTS
Admission and examination requirementsEnrolled in this course • Minimum  80% attendance in lecture
Recommended prerequisitesDiscrete Mathematics, Elementary Linear Algebra, Calculus I, Introduction to Real Analysis I
Media employeda whiteboard and projector
Forms of assessmentMidterm exam 20%, Final exam 30%, Quiz 20%, Structured assignment 20%, Activeness 10%
Intended Learning Outcome
Students Able to solve problems (C4) related to group, ring and fields as well express the results (A5) in spoken and written language  
Module content
Lecture (Class Work) 1. Sets, Mapping and Binary Operations 2. Group 3. Permutation Group 4. Abel Group 5. Subgroup 6. Equivalent Relations 7. Cyclical Groups 8. Group Homomorphism 9. Cosien Group 10. Cosien Group Homomorphism 11. Ring and Field 12. Ring and Field Homomorphism  
Recommended Literature John R. Durbin (2008), Modern Algebra: An Introduction, 6th Edition, Wiley .I.N. Herstein (2008), Topics in Algebra.Muchlis, Ahmad dan Puji Astuti, Aljabar IG. Elfiyanti. Modul Perkuliahan Pengantar Aljabar Abstrak

FST6094123 Partial Differential Equations

Module NamePartial Differential Equations
Module level, if applicableUndergraduate
Module Identification CodeFST 6094123
Semester(s) in which the module is taught5
Person(s) responsible for the moduleMuhammad Manaqib, M.Sc. Irma Fauziah, M.Sc.
Language Indonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursCollaborative learning & discussion-based learning, structured activities (homework, quizzes, case-based/project-based assignments).
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesOrdinary Differential Equations
Media employedClassical teaching tools with white board and PowerPoint presentation
Forms of assessmentMidterm exam 30%, Final exam 40%, Quiz 10%, Structured assignment 20%
Intended Learning Outcome
Students are expected to be able to solve problems related to the concepts of partial differential equations and find solutions to partial differential equations.  
Module content
Lecture (Class Work) Boundary and initial conditions Method of Characteristics: first order linear and quasi-linearinitial value problems. Fourier Series Sturm Liouville eigenvalue problems Method of Separation of variables: Initial boundary value problems parabolic,hyperbolic, and elliptic types The Fourier Integral and solution of Initial boundary value problems on an infinite interval The Fourier Transform and solution of Initial boundary value problems on a semi-infinite interval
Recommended Literatures Walter A. Strauss (1992). Partial Diffrential Equations, An Introduction. New York: John Wiley & sons, Inc. Boyce, WE, Di Prima, RC. Elementary Differential Equations and Boundary Value Problems. 6ed, John Wiley & Sons.

UIN6000208 Research Methodology

Module NameResearch Methodology
Module level, if applicableUndergraduate
Module Identification CodeFST 6000208
Semester(s) in which the module is taught3
Person(s) responsible for the moduleDr. Taufik Sutanto
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesElementary StatisticsExploratory Data Analysis
Media employedLMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessmentAssignments 30%, Quiz 20%, Projects 50%
Intended Learning Outcome
Provides basic knowledge on how to plan and carry out scientific research. Includes discussion of the meaning, function of research and research methods which include knowledge about selecting problems, compiling research designs, determining samples, collecting data, processing and analysing data, and writing reports.
Module content
Lecture (Class Work) 1. Introduction to research methods: types of research, stages in conducting research. 2. Literature Study: Determining research topics, formulating problems, setting goals, and identifying research contributions 3. The design of the research questionnaire. 4. Side scheme. 5. Data analysis. 6. Referring system. 7. Writing research reports.
Recommended Literatures Dane, F.C. 1990. Research Methods. Brooks/Cole Publishing Company. Belmont California. Dawson, C. “Practical research methods: A user friendly guide to research. 3 Newtec Place.” (2002). Sheperis, Carl J., J. Scott Young, and M. Harry Daniels. Counseling research: Quantitative, qualitative, and mixed methods. Pearson, 2016.

FST6094126 Mathematical Modeling

Module NameMathematical Modeling
Module level, if applicableUndergraduate
Module Identification CodeFST 6094126
Semester(s) in which the module is taught6
Person(s) responsible for the moduleMuhammad Manaqib, M.Sc. Irma Fauziah, M.Sc.
Language Indonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursProject-based learning & problem-based learning, structured activities (homework, quizzes, case-based/project-based assignments).
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks = 42 h Independent study: 3 x 60 min x 14 wks = 42 h Exam: 3 x 50 min x 2 times = 5 h; Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesMulti Variable Calculus , Elementary Linear Algebra, Ordinary Differential Equations
Media employedClassical teaching tools with white board and PowerPoint presentation
Forms of assessmentMidterm exam 30%, Final exam 40%, Quiz 10%, Structured assignment 20%
Intended Learning Outcome
Students are expected to be able to design mathematical models of problems or phenomena in real life problems and present the results using spoken and written language.  
Module content
Lecture (Class Work) Stages of mathematical modeling. Logistic and constant population growth model. Mathematical model of epidemic disease. SIR disease epidemic model equilibrium point. The stability of the SIR model around the equilibrium point and its interpretation. Mathematical model of the vehicle routing problem. Completion of the mathematical model of the vehicle routing problem and its interpretation. M/M/1 and M/M/s queuing models. Implementation of M/M/1 and M/M/s queuing models. Mathematical models to solve problems in everyday life.
Recommended Literatures Maki, Daniel P. Thompson, Maynard. Mathematical Models and Applications. 1973. Prentice-Hall. United States of America. Wiggins, Stephen. 2003. Introduction to Applied Non-Linear Dynamical Systems and Chaos Second Edition. New-York: Spinger. Verhulst, Ferdinand. 1990. Non-Linear Differential Equation and Dynamical Systems . Berlin: Spinger. Golden, Bruce. 2008. The Vehicle Routing Problem: Latest Advances and New Challenges. New York: Springer. Hillier, F. dan Lieberman, G. 2001. Introduction to Operations Research Seventh Edition, New York: McGraw-Hill. Taha, Hamdy A. 2007. Operations Research: An Introduction Eighth Edition. New Jersey: Pearson Prentice Hall. Manaqib, Muhammad. 2021. Modul Perkulihan Pemodelan Matematika. Tangerang Selatan: FST UIN Syarif Hidayatullah Jakarta.

FST6094125 Introduction to Graph Theory

Module NameIntroduction to Graph Theory
Module level, if applicableUndergraduate
Module Identification CodeFST 6094125
Semester(s) in which the module is taught6
Person(s) responsible for the moduleDr. Nur Inayah, M. Si Dr. Gustina Elfiyanti, M.Si
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours  
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisitesCalculus I and Mathematics Discrete
Media employedClassical teaching tools with white board and Power Point presentation
Forms of assessmentMidterm exam 40%, Final exam 40%, Quiz 10%, Structured assignment 10%
Intended Learning Outcome
  After completing the course, the students will have the ability to explain the basic concept of graph and implement it on the theorem construction of graph labelings
Module content
Lecture (Class Work) 1. Basic Graphs 2. Directed and Undirected Graphs 3. Connected and Unconnected Graphs 4. Some Graphs 4.1.  Path and Cycle 4.2.  Tree 4.3.  Bipartit dan Complete Bipartit 4.4.  Wheel dan Fan 4.5.  Prism and graf Antiprism 4.6.  Peterzen 4.7.  Shackle 4.8.  Amalgamation 5. Isomorfics, Matrics and  Connectivity 6. Euler Tours and  Hamilton Cycle 7. Pelabelan Ajaib dan Anti Ajaib  
Recommended Literatures Nora Hartsfield and  Gerhard Ringel, 1994, Pearls in Graph Theory, Academic Press, Harcourt Brace & Company Publishers, New YorkJ. A. Bondy and U.S.R. Murty, 1976, Graph Theory With Applications, Elsevier Science Publishing, New York W. D. Wallis, 2001,  Magics Graphs,  Birkhauser Publisher, Boston  

FST6094124 Optimisation Methods

Module NameOptimisation Methods
Module level, if applicableUndergraduate
Module Identification CodeFST6094124
Semester(s) in which the module is taught3
Person(s) responsible for the moduleMuhaza Liebenlito
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesCalculus IIIElementary Linear Algebra
Media employedLMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessmentAssignments 30%, Quiz 20%, Projects 50%
Intended Learning Outcome
Student are able to express computational problems from various application areas as optimisation problems. Be familiar with most frequently used algorithms for optimisation problems. Understand the ideas underlying the algorithms or methods mentioned.
Module content
Lecture (Class Work) 1. Introduction to optimisation problems 2. Linear programming using Simplex method 3. Revised Simplex method 4. Dual problems and sensitivity analysis 5. Nonlinear programming 6. Neccesary and sufficient condition for unconstrained problems 7. Newton, Steepest Descent, and Conjugate-Gradient for solving unconstrained problems 8. Neccesary and sufficient condition for constrained problems 9. Lagrange method, Sequential Quadratic and Penalty & Barrier method
Recommended Literatures 1. D.G. Luenberger, Y. Ye. Linear and Nonlinear Programming Third Edition. Springer, 2008. 2. S. G. Nash, A. Sofer. Linear and Nonlinear Programming. McGraw-Hill, 1996.

UIN6000207 Community Service Program

Module NameCommunity Service Program
Module level, if applicableUndergraduate
Module Identification CodeUIN6000207
Semester(s) in which the module is taught7
Person(s) responsible for the moduleCenter for Community Service UIN Syarif Hidayatullah Jakarta
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe students has 1 month preparation, 1 months stay and work in the village, and 1 month making a report, including final test.
WorkloadIndependent Study 4 x 170 m x 4 wks x  6 months Total 272 hours
Credit points4 Credit Hours ≈ 9.066 ECTS
Admission and examination requirementsEnrolled in this course
Recommended prerequisitesThe student has to register the Center for Community Service to the study load card (KRS) in Semester VI. The Center for Community Service can be done during free time between the sixth and the seventh semesters
Media employedPaper, Laptop/Computer, and village.  
Forms of assessmentThe final mark will be decided by considering some criteria involving the independence and team work ability, attitude and ethic, substance of the Center for Community Service. The components will be taken from the lecturers (during preparation until test at the end of the activities) and the chair of the village where the students work for the Center for Community Service. A: 80-100; B: 70-79,9; C: 60- 69,9; D: 50-59,9; E: <50
Intended Learning Outcome
After completing this course, the students should have: strong insight in local wisdom and high sensitivity to the problems in the society
Module content
Topic is appointed by university or group of students.
Recommended Literatures Books related to the topics.

UIN6000206 Internship

Module NameInternship
Module level, if applicableUndergraduate
Module Identification CodeUIN6000206
Semester(s) in which the module is taught7
Person(s) responsible for the moduleChair of Bc-Math
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursStudents are supervised by supervisors (lecturer and field supervisor)
WorkloadIndependent Study 4 x 170 m x 4 wks x  6 months Total 272 hours
Credit points4 Credit Hours ≈ 9.066 ECTS
Admission and examination requirementsEnrolled in this course
Recommended prerequisites 
Media employedPaper, Laptop/Computer, and village.  
Forms of assessmentInternship  examination  are  conducted  after  student  completes his  internship  report.    The  elements  of  evaluation  consist  of  a feasibility  assessment  topics,  the  level  of  student  participation during  internship,  academic  writing,  presentation,  and  oral  test about content of internship report
Intended Learning Outcome
1. Apply the basics of mathematics and statistics to the problems in the field 2. Solve the problems in the field by using mathematics and statistics 3. Develop a good communication and teamwork 4. Write internship report in a comprehensive manner
Module content
Topic is appointed by university or group of students.
Recommended Literatures Books related to the topics.

UIN6000312 Final Project

Module NameFinal Project
Module level, if applicableUndergraduate
Module Identification CodeUIN 6000312
Semester(s) in which the module is taught8
Person(s) responsible for the moduleChair of Bc-Math
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursStudents are supervised by supervisors or more
WorkloadIndependent Study 6 x 170 m x 4 wks x  6 months = 408 h Exam : 6 x 30 m= 3 h Total : 411 h
Credit points6 Credit Hours ≈ 13,7 ECTS
Admission and examination requirementsTo be able to take the final exam students must complete courses (minimum 138 credits) without having a D grade.
Recommended prerequisites 
Media employedPaper, Laptop/Computer  
Forms of assessmentFinal project examinationsare conducted after the student completes his final project manuscript. The elements of evaluation consist of feasibility assessment topics, academic writing, presentation, and oral test about the content of the final project. final exam using the agreed system 80 ≤ A ≤100; 70 ≤ B < 80; 60 ≤ C < 70; 60 ≤ D < 50.
Intended Learning Outcome
Apply the knowledge, experience, and skills learned in Bc-Mathematics to the chosen topic and case Write scientific papers in a comprehensive manner Studentshave professional ethics and soft skill: presentation, communication, discussion, and reason
Module content
The topic and content of the final project are discussed with the supervisor before starting the work
Recommended Literatures Books related to the topics.

UIN6000313 Seminar

Module NameSeminar
Module level, if applicableUndergraduate
Module Identification CodeUIN 6000313
Semester(s) in which the module is taught8
Person(s) responsible for the moduleChair of Bc-Math
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursFinal project presentation and discussion Students are supervised by supervisors or more
WorkloadIndependent Study 1 x 170 m x 4 wks x  6 months = 68 h Exam : 2 x 60 m= 2 h Total : 70 h
Credit points1 Credit Hours ≈ 2.33 ECTS
Admission and examination requirementsTo be able to take the final exam students must complete courses (minimum 138 credits) without having a D grade.
Recommended prerequisites 
Media employedPaper, Laptop/Computer  
Forms of assessmentAssessment includes: the ability to deliver seminar papers, the ability to answer and the accuracy of answers, language and attitude, paper format, timeliness
Intended Learning Outcome
Students are able to arrange and submit the results of their final assignment studies in scientific forums
Module content
The topic and content of the final project are discussed with the supervisor before starting the work
Recommended Literatures Books related to the topics.


 

II.             COMPLEMENTARY COMPETENCIES

FST6094306 Categorical Data Analysis

Module NameCategorical Data Analysis
Module level, if applicableUndergraduate
Module Identification CodeFST6094306
Semester(s) in which the module is taught5
Person(s) responsible for the moduleMadona Yunita Wijaya
LanguageIndonesian
Relation in CurriculumElective course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesElementary Statistics and Linear Model
Media employedClassical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessmentAssignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students will be able to utilize software (R) to conduct statistical tests and fit statistical models for categorical data, particularly for binary outcome. In addition, students will have the ability to interpret model results properly and draw conclusions in case studies.
Module content
Lecture (Class Work) Introduction to categorical data Inference for binomial proportion: Score test, Wald test, LRT Contingency table Measure of association for contingency table: proportion difference, relative risk, odds ratio Test for independence Generalized linear model for discrete data Simple logistic regression model Multiple logistic regression model
Recommended Literatures 1.    An Introduction to Categorical Data Analysis (2nd edition) by Alan Agresti. Published by John Wiley & Sons, 2007. ISBN-10: 0471226181. 2.    S-PLUS (and R) Manual to Accompany Agresti’s Categorical Data Analysis (2002) 2nd edition by Laura A. Thompson, 2006.

FST6094304 Introduction to Data Mining

Module NameIntroduction to Data Mining
Module level, if applicableUndergraduate
Module Identification CodeFST 6094304
Semester(s) in which the module is taught3
Person(s) responsible for the moduleDr. Taufik Sutanto
LanguageIndonesian
Relation in CurriculumElective course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesExploratory Data AnalysisLinear ModelsMultivariate Statistics
Media employedLMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessmentMidterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
students are able to identify information and insights hidden in data using various statistical and machine learning methods and provide recommendations that can be utilized by users.
Module content
Lecture (Class Work) Introduction to Data Mining Data Mining Process Association Rule Clustering Analysis Correlation and Regression Classification Models Ensemble Models Learning from Imbalance Problems
Recommended Literatures Data Mining: Concepts and Techniques by J Han, M Kamber & J Pei, 2012, 3rd edition, Morgan Kaufmann. Aggarwal, C. C. (2015). Data mining: the textbook. Springer. P.Cabena, P. Hadjinian, R. Stadler, J. Verhees, and A. Zanasi. Discovering Data Mining: From Concept to Implementation. IBM, 1997 U. Fayyad, G. Piatetsky-Shapiro, and P. Smith. From data mining to knowledge discovery. AI Magzine,Volume 17,  pages 37-54, 1996. Barry, A. J. Michael & Linoff, S. Gordon. 2004. Data Mining Techniques. Wiley Publishing, Inc. Indianapolis : xxiii + 615 hlm. Malik, U., Goldwasser, M., & Johnston, B. (2019). SQL for Data Analytics: Perform fast and efficient data analysis with the power of SQL. Packt Publishing Ltd. Vanderplas, J. T. (2016). Python data science handbook: tools and techniques for developers. O’Reilly. Bishop, C. M. (2006). Pattern recognition and machine learning. springer. Simovici, D. (2018). Mathematical Analysis for Machine Learning and Data Mining. World Scientific Publishing Co., Inc. Zheng, A. (2015). Evaluating machine learning models: a beginner’s guide to key concepts and pitfalls.

FST6091107 Database System

Module NameDatabase System
Module level, if applicableUndergraduate
Module Identification CodeFST6091107
Semester(s) in which the module is taught5
Person(s) responsible for the moduleMohamad Irvan Septiar Musti, M.Si
LanguageIndonesian
Relation in CurriculumElective course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesAlgorithms and Data Structures
Media employedLMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessmentMidterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
After completing this course, students will have the ability to implement and manage databases to handle real-world problems. With these abilities, students are expected to be more prepared to face real-world challenges in database management and can provide positive contributions to solving problems and improving database system efficiency in various application contexts.
Module content
Lecture (Class Work) Pendahuluan : Sistem Basis Data Sistem File, Konsep Basis Data dan DBMS Pemodelan Basis Data Model Entity-Relationship (ERD) Model Relasional Normalisasi dan Denormalisasi Study Kasus : Membuat ERD, Model Relasional, Normalisasi Basis Data Pemograman Basis data (SQL), ORM dan Metode Akses Indexing Query Processing dan Optimisasi Query Management Transaksi dan Locking Parallel basis datas & Streaming basis datas NoSQL  
Recommended Literatures Hellerstein, Joseph, and Michael Stonebraker. Readings in Basis data Systems (The Red Book). 4th ed. MIT Press, 2005. ISBN: 9780262693141. Ramakrishnan, Raghu, and Johannes Gehrke. Basis data Management Systems. 3rd ed. McGraw-Hill, 2002. ISBN: 9780072465631. Henry F. Korth, Abraham Silberschatz. Basis data system concepts 6th Edition. McGraw-Hill, 2011. C. J. Date. An Introduction to Basis data Systems 8th. Pearson Education, 2006 Jeffrey Ullman, Jennifer Widom, and Hector Garcia-Molin. Basis data Systems: Pearson New International Edition: The Complete Book, 2013.

FST6094311 Spatial Statistics

Module NameSpatial Statistics
Module level, if applicableUndergraduate
Module Identification CodeFST6094311
Semester(s) in which the module is taught5
Person(s) responsible for the moduleMahmudi, M.Si  
Language Indonesian
Relation in CurriculumStatistics  specialization courses for the mathematics undergraduate program  
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks = 42 h Independent study: 3 x 60 min x 14 wks = 42 h Exam: 3 x 50 min x 2 times = 5 h; Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum  80% attendance in lecture
Recommended prerequisitesElementary Statistics
Media employeda whiteboard and projector
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Students able to solve (C4) problems related to spatial data using kriging method  and be able to present (A5) the results
Module content
Lecture (Class Work)   Introduction of spatial dataRegionalized variablesVariogramExperimental variogramsDispersion Simple KrigingOrdinary KrigingUniversal Kriging
Recommended Literature Margaret Armstrong , Basic Linear Geostatistics, Springer-Verlag,  New York, 1998Roger S. Bivand, Edzer J. Pebesma, and Virgilio Gomez-Rubio, Applied Spatial Data Analysis with R, Springer, New York. 2011

FST6094312 Control Statistics Quality

Module NameControl Statistics Quality
Module level, if applicableUndergraduate
Module Identification CodeFST 6094312
Semester(s) in which the module is taught5
Person(s) responsible for the moduleMadona Yunita Wijaya
LanguageIndonesian
Relation in CurriculumElective course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks = 42 h Independent study: 3 x 60 min x 14 wks = 42 h Exam: 3 x 50 min x 2 times = 5 h; Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesElementary Statistics
Media employedClassical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessmentAssignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
Students are able to calculate (C3) the capability of a production process in relation to quality control. Students can select (C5) the appropriate type of acceptance sampling based on real-world quality control issues.
Module content
Lecture (Class Work) Introduction: – Meaning of quality control – Quality control planning and processes – Objectives of quality control – History/evolution of quality control Total Quality Control (TQC) / Integrated Quality Control: – Basic Mentality – System Management – Seven Tools – New seven tools – Kaizen Statistical Quality Control: – Statistical concepts and probability – Understanding variation in the production process – Control charts and their types – X, R, σ control charts – CUSUM charts – EWMA charts – Out-of-control situations on X, R charts – Analysis of control limits versus required specification limits Attribute Control Charts: – Basics of classification – Control charts for p, np, c, u – Out-of-control situations on p charts – Data with linear trend Acceptance Sampling: – Definition of acceptance sampling – Types of acceptance sampling Single Sampling Plans: – Operating Characteristic Curve (OC curve) – Acceptance sampling with sample size – Sensitivity of acceptance sampling – Producer’s risk and consumer’s risk Double Sampling Plans & Dodge-Romig Tables: – Understanding double sampling plans – Understanding Average Outgoing Quality Level (AOQL) – Selecting sampling plans to minimize Average Total Inspection (ATI) – Understanding Dodge-Romig tables Mil-STD-105D/AB C-STD/105 Tables: – Acceptance sampling using ABC-STD-105 – Transition of inspection types
Recommended Literatures Main Reference:  (1) Montgomery, Douglas C, “Introduction to statistical Quality Control”, John Wiley & Sons Inc, New York , 2005       Additional references:    (2) Grant & Leavenworth, “Statistical Quality Control”, MG Hill, New York , 1991.  (3) Bank, Jerry, Principal Of Quality Control ; John Wiley & Sons, 1989    

FST6094303 Non Statistics Parametric

Module NameNonparametric Statistics
Module level, if applicableUndergraduate
Module Identification CodeFST6094303
Semester(s) in which the module is taught5
Person(s) responsible for the moduleMadona Yunita Wijaya
LanguageIndonesian
Relation in CurriculumElective course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks = 42 h Independent study: 3 x 60 min x 14 wks = 42 h Exam: 3 x 50 min x 2 times = 5 h; Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesElementary Statistics
Media employedClassical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessmentAssignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students will be able to compare and contrast parametric and nonparametric tests. The students are able to identify the appropriate nonparametric testing procedures based on type of outcome variables to solve various statistical problems and interpret the results properly.
Module content
Lecture (Class Work) An Introduction to nonparametric statistics One sample nonparametric method Comparing two and more than two related samples Comparing two and more than two unrelated samples Measures of rank correlation (Spearman’s rho and Kendall’s Tau) Test for nominal scale data
Recommended Literatures Hollander, M., Wolfe, D. A., and Chicken, E. (2014). Nonparametric Statistical Methods, 3rd Edition. John Wiley & Sons, Inc  

FST6094305 Introduction to Risk Theory

Module NameRisk Theory
Module level, if applicableUndergraduate
Module Identification CodeFST6094308
Semester(s) in which the module is taught5
Person(s) responsible for the moduleDhea Urfina Zulkifli, M.Si.
LanguageIndonesian
Relation in CurriculumActuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several discussion groups. Each group was assigned to work on.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisitesNone
Media employedClassical teaching tools with glass whiteboard and PowerPoint presentation
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Able to comprehend the concepts of actuarial model characteristics, discrete distribution models, continuous distribution models, distribution error classifications, coverage modifications, aggregate loss models, and risk measures.
Module content  
Lecture (Class Work) Random Variable ReviewRandom Variable ReviewDiscrete Distribution ModelContinuous Distribution ModelDistribution Tail Classification Coverage ModificationAggregate Loss ModelRisk Measures
Recommended Literatures Klugman AK, Panjer HH, Willmot, GE. 2019. Loss Models: From Data to Decisions. 5th edition. John Wiley & Sons Inc, Hoboken, New Jersey. Dickson DCM. 2005. Insurance Risk and Ruins. Cambridge University Press, Cambridge, United Kingdom. Gray JG, Pitts SM. 2012. Risk Modelling in General Insurance. Cambridge University Press, Cambridge, United Kingdom.  

FST6094308 Introduction to Pension Plan

Module NameIntroduction to Pension Plan
Module level, if applicableUndergraduate
Module Identification CodeFST6094308
Semester(s) in which the module is taught5
Person(s) responsible for the moduleIrma Fauziah, M.Sc
LanguageIndonesian
Relation in CurriculumActuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several discussion groups. Each group was assigned to work on.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisitesNone
Media employedClassical teaching tools with glass whiteboard and PowerPoint presentation
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Capable of solving (C4) mathematical problems related to normal cost and actuarial liability valuation with several alternative actuarial methods for pension funds using basic mathematical concepts and able to present (A5) the results.
Module content  
Lecture (Class Work) History of development and government policies related to pension funds in Indonesia Defined contribution pension programs and defined benefit pension plans Pension fund notation and terminology as well as pension fund decrement opportunities. Life annuity based on salary. Normal cost and actuarial liability from the Traditional Unit Credit method Normal cost and actuarial liability from the Projected Unit Credit method Normal cost and actuarial liability from the Entry Age Normal dollar level method Normal cost and actuarial liability from the Entry Age Normal level percent method Supplemental costs of several alternative actuarial calculation methods for pension funds
Recommended Literatures Main : Aitken, H. William, Pension Funding and Valuation, 2th Edition, Waterloo University, ACTEX Publication, 2000. Supporting: Financial Services Authority, College Literacy Series Pension Program, Consumer Protection Education Commission of the Financial Services Authority, Jakarta, 2019.Financial Services Authority, Pension Fund Statistics, Directorate of Non-Bank Financial Statistics and Information of the Financial Services Authority, Jakarta, 2021.[https://www.ojk.go.id/id/kanal/iknb/data-dan-statistik/dana-pensiun/Pages/Buku-Statistik-Dana-Pensiun-2021.aspx]Winklevoss E. Howard, Pension Mathematics with Numerical Illustrations, Second Ed, University of Pennsylvania Press, 1977  

FST6094302 Introduction to Actuarial Mathematics

Module NameIntroduction to Actuarial Mathematics
Module level, if applicableUndergraduate
Module Identification CodeFST 6094302
Semester(s) in which the module is taught5
Person(s) responsible for the moduleIrma Fauziah, M.Sc
LanguageIndonesian
Relation in CurriculumActuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several discussion groups. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisiteshas taken calculus 1, introduction to financial mathematics and mathematical statistics 1 courses
Media employedClassical teaching tools with glass whiteboard and PowerPoint presentation
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Students able to solve (C4) problems related to the modeling of net premiums and premium reserves from single life insurance and be able to present (A5) the results
Module content
Lecture (Class Work)   Survival FunctionKumulative Distribution Function of Curtate Future Life Time and Future Lifetime Random VariableProbability of surviving, death probability, dan force of mortalityMortality table with and without selection effectTypes of Single Life insurance Single life insurance with increasing or decreasing insuranceInsurance with multiple benefits payments or multiple premium paymentContinuous and discrete life annuity and its typesLife annuity with payment variationsRelation between single life annuity and single life insuranceAnnual premium of single life insurance and premiums paid several periods in 1 yearSingle life insurance reserves with Retrospektive and Prospektive methods  
Recommended Literatures Main : Bowers, N.L. dkk., 1997.Actuarial Mathematics, Society of Actuaries   Supporting: Cunningham.J.R dkk. 2006. Model for Quantifying Risk, Second Edition. ACTEX Publication.Effendie A.R dkk. 2015. Matematika Aktuaria. Edisi 1. Penerbit Universitas TerbukaGerber, H.U.1997. Life Insurance Mathematics. Springer. Larson, RE and Gaumnitz, EA,. 1962. Life Insurance Mathematics. John Wiley and Son :  New York

FST6094324 Introduction to Microeconomics

Module NameMicroeconomics
Module level, if applicableUndergraduate
Module Identification CodeFST6094324
Semester(s) in which the module is taught5
Person(s) responsible for the moduleIrma Fauziah, M.Si.
LanguageIndonesian
Relation in CurriculumActuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisitesNone
Media employedClassical teaching tools with white board and PowerPoint presentation
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Capable of explaining (C2) microeconomic activity patterns, analyzing (C4) curves related to economic activities, and presenting (A5) the results both orally and in writing
Module content
Lecture (Class Work) Field of Study in EconomicsEconomic Activity PatternsEconomic Issues and Economic System RegulationDemand, Supply, and Market EquilibriumElasticity of Demand and SupplyApplications of Demand and Supply TheoryConsumer Behavior Theory: Utility TheoryConsumer Behavior Theory: Analysis of Indifference CurvesProduction Theory and Company ActivitiesProduction Cost TheoryPerfect Competition MarketMonopolyMonopolistic CompetitionOligopolyDemand for Factors of ProductionWage Determination in the Labor MarketRent, Interest, and ProfitsFree Markets and Government Policies
Recommended Literatures Main : Sadono Sukirno, 2016, Microeconomics (Introductory Theory), 3rd edition, PT. Raja Grafindo Persada: Jakarta. Supporting: McGraw Hill, by Samuelson, Nordhaus; translated by Nur Rosyidah, Anna Elly, Bosco Carvallo; edited by Siti Saadah, 2003. Microeconomics 17th Edition, Jakarta: Media Global Edukasi.    

FST6094307 Introduction to General Insurance

Module NameIntroduction to General Insurance
Module level, if applicableUndergraduate
Module Identification CodeFST6094307
Semester(s) in which the module is taught5
Person(s) responsible for the moduleDhea Urfiba Zulkifli, M.Si  
Language Indonesian
Relation in CurriculumActuarial specialization courses for the Mathematics undergraduate program  
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesIntroduction to Mathematical Finance
Media employeda whiteboard and and projector
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Students are able to analyze financial risks in both the banking and non-banking industries.  
Module content
Lecture (Class Work) Claim-frequency Distribution Claim-severity Distribution Aggregate-loss Models Risk Measures Ruin Theory
Recommended Literatures Tse, Yiu-Kuen, Nonlife Actuarial Models: Theory, Methods, and Evaluation, 2009, Cambridge Univerity Press.

FST6094309 Introduction to Sharia Insurance

Module NameIntroduction to Sharia Insurance
Module level, if applicableUndergraduate
Module Identification CodeFST6094309
Semester(s) in which the module is taught5
Person(s) responsible for the moduleMahmudi, M.Si  
Language Indonesian
Relation in CurriculumActuarial specialization courses for the Mathematics undergraduate program  
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites 
Media employeda whiteboard and and projector
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Students are able to construct and simulate sharia insurance model.  
Module content
Lecture (Class Work) Definition and principles of Sharia insuranceDifferences between Sharia Insurance and Conventional InsuranceWakala ModelMudharabah ModelMudharabah – Wakala ModelReturn of InversmentSimulation of Sharia Insurance Models
Recommended Literatures 1.M. S. Sula. 2004. Asuransi Syariah (Life and General) Konsep dan Sistem Operasional. Jakarta: Gema Insani. 2.M. A. Suma. 2006. Asuransi Syariah dan Asuransi Konvensional; Teori, Sistem, Aplikasi dan Pemasaran. Ciputat: Kholam Pusdishing. 3.O. Kurniandi. 2005. Stochastic Models for Premium Calculation under Syariah Law. PT. MAA Life Assurance, Indonesia. R. Cahyandari, et. Al. 2020. Table of Integration Model for Motor Vehicle Sharia Insurance. InPrime Indonesian Journal of Pure and Applied Mathematics, Vol. 2, pp. 59–64

FST6094316 Analysis of Social Media

Module NameAnalysis of Social Media
Module level, if applicableUndergraduate
Module Identification CodeFST 6094316
Semester(s) in which the module is taught6
Person(s) responsible for the moduleDr. Taufik Sutanto
LanguageIndonesian
Relation in CurriculumElective course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesExploratory Data AnalysisLinear ModelsMultivariate Statistics
Media employedLMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessmentMidterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
These learning outcomes aim to provide students with a well-rounded skill set that can be applied in professional settings where social media analysis is valuable, such as marketing, business intelligence, research, and strategic planning.
Module content
Lecture (Class Work) Introduction and the Importance of Social Media Analysis Understanding Major Social Media Platforms Data Collection Methods Data Analysis Techniques Social Media Monitoring and Listening Ethical Considerations Reporting and Communication Guest Speakers and Industry Applications Student Presentations and Projects Future Trends and Conclusion.
Recommended Literatures “Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More” by Matthew A. Russell “Social Media Mining: An Introduction” by Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu “Social Media Analytics: Techniques and Insights for Extracting Business Value Out of Social Media” by Matthew Ganis

FST6094314 Time Series Analysis

Module NameTime Series Analysis
Module level, if applicableUndergraduate
Module Identification CodeFST6094314
Semester(s) in which the module is taught6
Person(s) responsible for the moduleMadona Yunita Wijaya
LanguageIndonesian
Relation in CurriculumElective course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesElementary Statistics and Linear Model
Media employedClassical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessmentAssignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students should become skillful in analyzing and modeling of stochastic process ARIMA(p,d,q) model. Considered methods and models should be mastered by practice using real-world data and implemented with statistical software such as RStudio.
Module content
Lecture (Class Work) Motivating examples and characteristics of time series dataStationarityAutocovariance, autocorrelation, and partial autocorrelation functionStationary and non-stationary time series: AR(p), MA(q), ARMA(p,q), ARIMA(p,d,q)Model identification: ACF, PACF, EACF, Information CriteriaModel estimation: Method of moment, LS, MLModel diagnosis: analysis of residual and overparametrizationCross validation in time series modelForecasting
Recommended Literatures 1.    Cryer, J.D. and Chan, K.S. (2008).Time Series Analysis With  Applications in R, Second Edition. New York: Springer. 2.    Shumway, R.H. and Stoffer, D.S. (2015). Time Series Analysis and Its Applications With R Examples, EZ Green Edition. 3.    Montgomery, D.C., Jennings, C.L., and Kulachi, M. (2008). Introduction to Time Series Analysis and Forecasting. New Jersey: John Wiley & Son, Inc

FST6094315 Biostatistics

Module NameBiostatistics
Module level, if applicableUndergraduate
Module Identification CodeFST6094315
Semester(s) in which the module is taught6
Person(s) responsible for the moduleMadona Yunita Wijaya
LanguageIndonesian
Relation in CurriculumElective course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesElementary Statistics & Linear Model
Media employedClassical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessmentAssignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
The students will be able to: Understand the role of biostatistics in public health or medical studiesUnderstand the principle of various study designs, and explain their advantages and limitationsIdentify appropriate tests to perform hypothesis testing and to fit relevant models to address quantitative problems from public health or medical studies, and interpret the output adequately  
Module content
Lecture (Class Work) Study designs Type of study designsClassification clinical trialsSample size determination Comparing one and two groups (continuous outcome): one sample population mean, paired sample t-test, independent sample t-test Comparing more than two groups (continuous outcome): one-way ANOVA & two-way ANOVA Comparing two groups (categorical outcome): Chi-square test, McNemar’s test, OR, RR Introduction to longitudinal data analysis: random intercept model, random intercept and slope model Introduction to survival analysis: Survival and Hazard functions, Kaplan-Meier  
Recommended Literatures Le, C. T. (2003). Introductory Biostatistics, John Wiley & Sons, Inc. Rosner, Bernard. (2006). Fundamentals of Biostatistics. 6th edition. Thomson Brooks/Cole  

FST6094327 High Performance Computing

Module NameHigh Performance Computing
Module level, if applicableUndergraduate
Module Identification CodeFST6094327
Semester(s) in which the module is taught6
Person(s) responsible for the moduleDr. Taufik Sutanto
LanguageIndonesian
Relation in CurriculumElective course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesExploratory Data AnalysisLinear ModelsMultivariate Statistics
Media employedLMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessmentMidterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
These learning outcomes collectively aim to equip students with the knowledge and skills necessary to design, implement, and optimize parallel algorithms for high-performance computing environments.
Module content
Lecture (Class Work) Understanding Parallel Architectures Parallel Programming Models Algorithm Design for Parallel Computing Parallelization Techniques High-Performance Computing Tools and Libraries Performance Analysis and Profiling Distributed Systems and Clusters Message Passing Interface (MPI) Load Balancing and Scalability Optimization Strategies Parallel I/O and Storage
Recommended Literatures “Introduction to High Performance Computing for Scientists and Engineers” by Georg Hager and Gerhard Wellein “Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers” by Barry Wilkinson and Michael Allen “High-Performance Computing: Modern Systems and Practices” by Thomas Sterling and Matthew Anderson “Programming Massively Parallel Processors: A Hands-on Approach” by David B. Kirk and Wen-mei W. Hwu “CUDA by Example: An Introduction to General-Purpose GPU Programming” by Jason Sanders and Edward Kandrot

FST6094310 Selective Capita

Module NameSelective Capita
Module level, if applicableUndergraduate
Module Identification CodeFST6094310
Semester(s) in which the module is taught6
Person(s) responsible for the module 
LanguageIndonesian
Relation in CurriculumElective course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites 
Media employedLMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessmentMidterm exam 20%, Final exam 30%, Quiz 10%, Projects 30%
Intended Learning Outcome
Mastering specialized topics in data science, actuarial science, or pure and applied mathematics.
Module content
Lecture (Class Work) Specialized topics in data science, actuarial science, or pure and applied mathematics.
Recommended Literatures Books and articles on specific topics in data science, actuarial science, or pure and applied mathematics.

FST6094322 Advanced Actuarial Mathematics

Module NameAdvanced Actuarial Mathematics
Module level, if applicableUndergraduate
Module Identification CodeFST6094322
Semester(s) in which the module is taught6
Person(s) responsible for the moduleIrma Fauziah, M.Sc
LanguageIndonesian
Relation in CurriculumActuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several discussion groups. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisitesHas taken Introduction to Actuarial Mathematics course
Media employedClassical teaching tools with glass whiteboard and PowerPoint presentation
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Able to solve (C4) problems related to modeling net premiums and premium reserves for multi-life insurance and able to present (A5) the results.
Module content  
Lecture (Class Work) Joint Life Status Last Survivor Status Multi-Life Life Insurance Multi-Life Life Annuity Premiums and Reserves for Multi-Life Insurance Makeham and Gompertz Mortality Laws Uniform Distribution of Death (UDD) Law Simple and Multiple Contingency Functions Simple Contingency Insurance and Annuities Reserves for Simple Contingency Insurance      
Recommended Literatures Utama : Cunningham.J.R dkk. 2006. Model for Quantifying Risk, Second Edition. ACTEX Publication Pendukung : Bowers, N.L. dkk., 1997.Actuarial Mathematics, Society of ActuariesEffendie A.R dkk. 2015. Matematika Aktuaria. Edisi 1. Penerbit Universitas TerbukaGerber, H.U.1997. Life Insurance Mathematics. Springer. Larson, RE and Gaumnitz, EA,. 1962. Life Insurance Mathematics. John Wiley and Son :  New York

FST6091911 Natural Language Processing

Module NameNatural Language Processing
Module level, if applicableUndergraduate
Module Identification CodeFST6091911
Semester(s) in which the module is taught5
Person(s) responsible for the moduleMuhaza Liebenlito, M.Si.
LanguageIndonesian
Relation in CurriculumCompulsory course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. Students are divided into several groups of discussion. Each group was assigned to work on a specific topic relevant to the lecture and presented in the class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisites– Data Mining – Basic Programming
Media employedLMS, Classical teaching tools with white board, and PowerPoint presentation
Forms of assessmentAssignments 30%, Quiz 20%, Projects 50%
Intended Learning Outcome
On completion of this subject the student is expected to: Identify basic challenges associated with the computational modelling of natural language; Understand and articulate the mathematical and/or algorithmic basis of common techniques used in natural language processing; Implement relevant techniques and/or interface with existing libraries; Carry out end-to-end research experiments, including evaluation with text corpora as well as presentation and interpretation of results; Critically analyse and assess text-processing systems and communicate criticisms constructively.
Module content
Lecture (Class Work) 1. Text classification and unsupervised topic discovery 2. Vector space models for natural language semantics 3. Structured prediction for tagging 4. Syntax models for parsing of sentences and documents 7. N-gram language modelling 8. Machine translation 9. Deep learning for NLP
Recommended Literatures 1. Daniel Jurafsky and James Martin. Speech and Language Processing (3rd Edition). Springer, 2023. 2. Dipanjan Sarkar. Text Analytics with Python. Apress/Springer, 2016.

FEB6084202 Introduction to Macroeconomics

Module NameIntroduction to Macroeconomics
Module level, if applicableundergraduate
Module Identification CodeFEB6084202
Semester(s) in which the module is taught6
Person(s) responsible for the moduleIrma Fauziah, M.Sc.
LanguageIndonesian
Relation in CurriculumActuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hoursCollaborative learning & discussion-based learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesNone
Media employedClassical teaching tools with white board and PowerPoint presentation
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Capable of explaining (C2) macroeconomic activity patterns, analyzing (C4) curves related to aggregate demand and aggregate supply, and articulating (A5) their findings in both oral and written language.
Module content
Lecture (Class Work) Introduction to MacroeconomicsEconomic Activity DeterminationMoney and Financial InstitutionsEconomic Policy in Closed and Open EconomiesEconomic Growth Concepts and Determinants  
Recommended Literatures Sadono Sukirno, 2015, Makroekonomi (Teori Pengantar), edisi ke-3, PT. Raja Grafindo Persada : Jakarta Mc Graw Hill, by Samuelson, Nordhaus. ; alih bahasa, Nur Rosyidah, Anna Elly, Bosco Carvallo ; penyunting, Siti Saadah , 2003. Ilmu Makroekonomi Edisi 17, Jakarta : Media Global Edukasi

FST6094320 Introduction to Actuarial Computing

Module NameIntroduction to Actuarial Computing
Module level, if applicableundergraduate
Module Identification CodeFST 6094320
Semester(s) in which the module is taught6
Person(s) responsible for the moduleDhea Urfina Zulkifli, M.Si
LanguageIndonesian
Relation in CurriculumActuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hoursDifferentiated learning & inquiry-based learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesNone
Media employedClassical teaching tools with white board and PowerPoint presentation
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Able to comprehend the introductory material of R software, survival models, life insurance, life annuities, and life insurance premiums using the R software.
Module content
Lecture (Class Work) Introduction to R software Survival model Life insurance Life annuity\ Life insurance premium  
Recommended Literatures Effendie, A.R., 2021, Matematika Aktuaria dengan Software R, Gadjah Mada University Press.

FST6094317 Introduction to Financial Computing

Module NameIntroduction to Financial Computing
Module level, if applicableundergraduate
Module Identification CodeFST 6094317
Semester(s) in which the module is taught6
Person(s) responsible for the moduleDhea Urfina Zulkifli, M.Si.
LanguageIndonesian
Relation in CurriculumActuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hoursProject-based learning & problem-based learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesNone
Media employedClassical teaching tools with white board and PowerPoint presentation
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Able to comprehend the introductory material of programming with R software, interest rates, investment yield rates, annuities, general annuities, loan repayments, bonds, spot rates, and forward rates, as well as asset-liability management, duration, and immunization using R software.
Module content
Lecture (Class Work) Introduction to programming with R software Interest rates Investment yield rates Annuities General annuities Loan repayments Bonds Spot rates and forward rates Asset-liability management, Duration, Immunization
Recommended Literatures Syaifudin, W.H., 2020, Matematika Finansial dengan Software R, Deepublish. Bodie, Kane, and Marcus, 2013, Investment. New York : McGraw-Hill. Hull, J.C., 2009, Options, Futures, and Other Derivatives. 7th Edition. New Jersey: Published by Prentice Hall. Capi´nski, M. and Zastawniak, T., 2003, Finance: An Introduction to Financial Engineering. London: Springer-Verlag. Sidarto, K.A. et.al, 2019, Matematika Keuangan, ITB Press.

FST6094328 Introduction to Statistical Computing

Module NameIntroduction to Statistical Computing
Module level, if applicableUndergraduate
Module Identification CodeFST6094328
Semester(s) in which the module is taught6
Person(s) responsible for the moduleMadona Yunita Wijaya
LanguageIndonesian
Relation in CurriculumElective course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesElementary Statistics and Linear Model
Media employedClassical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessmentAssignments (including quizzes and group project): 40%Midterm exam: 30%Final exam: 30%
Intended Learning Outcome
Students are able to use computational and graphic approaches to solve statistical problems. They can manipulate or modify data, present data using graphics, design and conduct simple Monte Carlo experiments, and can use resampling methods such as Bootstrap.
Module content
Lecture (Class Work) Introduction to R Dynamic and Reproducible Reporting with R Markdown Data Visualization Data Manipulation Inferential Statistics Random Number Generation Simulation of Distribution Models Monte Carlo Simulation Bootstrap Permutation Methods Jackknife Cross-validation
Recommended Literatures R for Data Science (Hadley Wickham dan Garett Grolemund, 2017) An Introduction to the Bootstrap (Bradley Efron dan Robert J. Tibshirani, 1993) Computational Statistics, 2nd Edition (Givens, G. H. dan Hoeting, J. A., 2005)

FST6094318 Introduction to Insurance Company Operation

Module NameIntroduction to Insurance Company Operations
Module level, if applicableundergraduate
Module Identification CodeFST6094318
Semester(s) in which the module is taught6
Person(s) responsible for the moduleIrma Fauziah, M.Si.
LanguageIndonesian
Relation in CurriculumActuarial specialization courses for the Mathematics undergraduate program
Teaching methods, Contact hoursDifferentiated learning & inquiry-based learning, class discussion, structured activities (homework, quizzes).
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesNone
Media employedClassical teaching tools with white board and PowerPoint presentation
Forms of assessmentMidterm exam 40%, Final exam 40%, Quiz 10%, Structured assignment 10%
Intended Learning Outcome
Capable of classifying (C3) the operations of conventional life insurance companies and Sharia-compliant life insurance, and able to present (A5) the results in both spoken and written language.
Module content
Lecture (Class Work) Understanding Life Insurance Risk and Insurance Insurance Contracts Insurance Policies Term Life Insurance Whole Life and Endowment Life Insurance New Generation Savings Insurance Unit Linked Insurance Products Pension and Annuity Programs Technical Aspects of Life Insurance Sharia Insurance Theory and Scholars’ Opinions on Insurance The Phenomenon of Usury (Riba) and Interest in the Operational System of Life Insurance in Eliminating Ambiguity (Gharar), Gambling (Maisir), and Usury (Riba) Operational Aspects of Insurance Companies Restructuring and Formation of Life Insurance Companies and Organizational Structures of Insurance Companies Marketing Strategies and Product Distribution Activities Life Insurance Underwriting Actuarial Functions Life Insurance Claim Administration
Recommended Literatures Bowers, N.L. dkk., 1997.Actuarial Mathematics, Society of ActuariesDasar-Dasar Asuransi : Jiwa, Kesehatan, dan Anuitas, Drs. H. Kasir Iskandar, MBA, AAIJ, DR.H. Noor Fuad, MSc., Ph.D., FLMI, Dr. Faustinus Wirasadi, FLMI, dan I Ketut Sendra, SH,MH,AAIJ, Jakarta : AAMAI Publisher, 2011 Asuransi Syariah (Life and General) : Konsep dan Sistem Operasional, Muhammad Syakir Sula, AAIJ, Depok : Gema Insani, 2004.Insurance Company Operation, 2nd Edition, Miriam Orsina, and Gene Stone, Atlanta : Life Office Management Association (LOMA), 2005.Operations of Life and Health Insurance Companies, 2nd Edition, Kenneth Huggins and Robert D. Land, LOMA 1994.

FST6094108 Multivariate Statistic

Module NameMultivariate Statistic
Module level, if applicableUndergraduate
Module Identification CodeFST 6094108
Semester(s) in which the module is taught3
Person(s) responsible for the moduleDr. Suma Inna, M.Si Mahmudi, M.Si
Language Indonesian
Relation in CurriculumCompulsory course 
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 75% attendance in lecture
Recommended prerequisitesCalculus II
Media employeda whiteboard and and projector
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
After completing the course, students have the ability Able to explain (C1) (A5) the concepts of parametric functions and scalar functions both verbally and in writing. Capable of solving problems (C4) related to partial derivatives of scalar functions and presenting the results logically and systematically, both verbally and in writing. Capable of solving problems (C4) related to vector functions and presenting the results logically and systematically, both verbally and in writing. Capable of solving problems (C4) related to double and triple integrals, line integrals, and surface integrals of multivariable functions, both verbally and in writing. Able to demonstrate (C4) the relationship between double integrals and line integrals, both verbally and in writing.
Module content
Lecture (Class Work) Parametric Functions: Limits and Continuity of Parametric Functions; Integral and Arc Length of Parametric Functions.Scalar Functions: Height Curves; Limits and Continuity; Partial Derivatives and Gradient Vectors; Differentiability; Total Differentials; Chain Rule; Directional Derivatives; Implicit Differentiation; Extrema of Functions; Lagrange MethodVector Functions: Divergence and Curl; Conservative Vector Fields; Chain Rule; Jacobian Matrix; Inverse of Vector Functions.Multiple Integrals: Double Integrals; Triple Integrals; Coordinate Transformation in Multiple Integrals (Polar, Curvilinear, Cylindrical, and Spherical Coordinates).Line Integrals: Line Integrals of Vector Fields; Relationship between Line Integrals
Recommended Literature James Stewart, Multivariable Calculus Seventh Edition, McMASTER UNIVERSITY AND UNIVERSITY OF TORONTO Wono Setya Budhi. Multivariable Calculus and Its Applications. ITB Bandung. Bandung

 

FST6094319 Sampling Techniques and Experimental Design

Module NameSampling Technique and Experimental Design
Module level, if applicableUndergraduate
Module Identification CodeFST6094319
Semester(s) in which the module is taught6
Person(s) responsible for the moduleAry Santoso
LanguageIndonesian
Relation in CurriculumElective course for undergraduate program in Mathematics
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by short discussion. At the end of the semester, students will work in groups on a small project on a specific topic relevant to the lecture.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirements• Enrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesElementary Statistics and Mathematical Statistics I
Media employedClassical teaching tools with white board, PowerPoint presentation, and practices in computer class
Forms of assessment·        Assignments (including quizzes and group project): 40% ·        Midterm exam: 30% ·        Final exam: 30%
Intended Learning Outcome –           Identify and explain the sample survey design dan experimental design –           Apply the sample survey and experimental design methods on real cases –           Apply the data preparation, processing, and analyzing
Module content
Sampling Technique The teaching materials consist of planning a survey, conducting a survey, methods of collecting survey data, selecting a sample survey, determining a sample size, analyzing survey data Experimental Design The teaching materials consist of planning an experiment, conducting an experiment, methods of collecting data, analyzing design experimental data Recommended Literatures
Recommended Literature 1.         Levy S. Paul, Lemeshow Stanley, 1990. Sampling of  populations  methods and applications, Third Edition, John Wiley & Sons INC.S 2.         Scheaffer Richard L, Mendenhall William,  Ott Lyman R, 2006. Elementary survey sampling, Sixth edition, Duxburry press boston. 3.         Monthgomery C Douglas. 2013. Design and Analysi of Experiments, Eight Edition. John Wiley & Son, Inc

FST 6094326 Number Theory

Module NameNumber Theory
Module level, if applicableUndergraduate
Module Identification CodeFST 6094326
Semester(s) in which the module is taught6
Person(s) responsible for the moduleMahmudi, M.Si  
Language Indonesian
Relation in CurriculumPure mathematics  specialization courses for the mathematics undergraduate program  
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
WorkloadLecture (class): (3 x 50 min) x 14 wks = 35 h Structured activities: 3 x 60 min x 14 wks =42 h Independent study: 3 x 60 min  x 14 wks = 42 h Exam:  3 x 50 min x 2 times = 5 h Total = 124 hours
Credit points3 Credit Hours (2-3) ≈ 4.133 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesDiscrete mathematics
Media employeda whiteboard and and projector
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Students able to solve (C4) problems related to disibility, greatest common divisors, congruences, and multiplicative functions, and be able to present (A5) the results.  
Module content
Lecture (Class Work) Binomial theorem and Disibility Greatest common divisors Euclidean algorithm Linear congruences and systems of linear congruences Application of congruences Wilson’s theorem and Fermat’s theorem Multiplicative functions
Recommended Literature Kennth H. Rosen. Elementary Number Theory and Its Application. Pearson College Division , 2011David M.Burton. Elementary Number Theory Sixth Editioon. New York : McGraw-Hill, 2007  

FST6094121 Linear Algebra

Module NameLinear Algebra
Module level, if applicableUndergraduate
Module Identification CodeFST 6094326
Semester(s) in which the module is taught6
Person(s) responsible for the moduleDr. Gustina Elfiyanti, M.Si  
Language Indonesian
Relation in CurriculumPure mathematics  specialization courses for the mathematics undergraduate program  
Teaching methods, Contact hoursThe course topics are delivered through lectures which are enriched with relevant examples and followed by a short discussion. Students are divided into five groups of discussion. Each group was assigned to work on a topic relevant to the lecture and presented in class.
WorkloadLecture (class): (4 x 50 min) x 14 wks = 46.67 h Structured activities: 4 h x 14 wks = 56 h Independent study: 4 h x 14 wks = 56 h  Exam:  200m x 2 (mid test and final test)  =  6.66 h Total =  165.33 h
Credit points4 Credit Hours ≈ 5.51 ECTS
Admission and examination requirementsEnrolled in this course • Minimum 80% attendance in lecture
Recommended prerequisitesCalculus 1 and Discrete mathematics
Media employeda whiteboard and and projector
Forms of assessmentMidterm exam 30%, Final exam 30%, Quiz 20%, Structured assignment 20%
Intended Learning Outcome
Able to solve problems (C4) related to systems of linear equations, matrices, vector spaces, and linear transformations, and articulate their results (A5) in both oral and written forms.  
Module content
Lecture (Class Work) Linear Equation Systems and Matrices Determinant Euclidean Vector Space General Vector Space Eigenvalues and Eigenvectors Dot Product Diagonalization and Quadratic Forms\ Linear Transformations
Recommended Literature Main (BS1): Howard Anton, Elementary Linear Algebra, Application Version, 11th ed., John Wiley & Sons, Inc , 2013 Support : 1. Wono Setya Budhi, Aljabar Linear, PT. Gramedia Pustaka Utama, 1995 2. Gustina Elfiyanti, Modul Perkuliahan Aljabar Linier Elementer, tidak diterbitkan.