Master Degree

Master Degree

Master of Mathematics Study Program (PSMM) has produced graduates who have contributed very significantly to the development of human resources, especially in eastern Indonesia. This is supported by the knowledge in analysis, algebra and computing related to the development of applied mathematics. Since its establishment in 2003, PSMM has graduated 472 masters of science until the 121st graduation period (March 2019). Currently, the lecturers in PSMM consist of 4 professors and 15 doctors who are competent in the fields of Max-Plus Algebra, Data Science, Graph Theory, Computational Fluid Dynamics, Dynamic Systems, Financial Mathematics, Bioinformatics, Systems Theory, Data Assimilation and Digital Image Processing. PSMM has collaborated with several universities, both within and outside the country, for example Shibaura Institute of Technology (Japan), University of Oxford (UK), University of Essex (UK), Technische Universiteit Delft (The Netherlands) and Universiti Malaysia Pahang (Malaysia). Since 2019, PSMM has been accredited A with SK number 4332/SK/BAN-PT/Akred/M/XI/2019.


To be the leading study program in internationally reputable Master of Mathematics Education, especially in the fields of analysis, algebra, and computing to support and develop applied mathematics


  1. To conduct Master of Mathematics Program in order to produce graduates who have morals and master in analysis, algebra, and computation and its applications 
  2. To develop national and international collaboration in mathematical education and research 
  3. To grow and maintain academic moral and ethic 
  4. To improve lecturer competence in order to be more creative and professional in the line of duty 


  1. To produce master’s in mathematics who have integrity and responsiveness to the change and advance in science and technology 
  2. To produce master’s in mathematics who have competence on Analysis and Applied Algebra, Modeling and Simulation, and Computer Science 
  3. To produce master’s in mathematics who have skills, motivation, and positive learning behavior, and also high work ethic on mathematical research and development 
  4. To produce master’s in mathematics who can give a contribution to solve real-life problem, especially that has relevance to energy, transportation, environment, maritime, and industry, as well as information technology. 

Alumni Profile

The graduates of MoMath are expected to work in the following areas:

  1. Finance, banking, and insurance: applying knowledge and skill in finance, banking, and insurance, based on mathematical and statistical methods
  2. System analyst and programmer: applying knowledge and skill of programming languages and algorithm analysis, and software engineering to develop software applications.
  3. Data analyst: applying knowledge and skill based on mathematical thinking in data processing and programming to analyze data and its application.
  4. Educator: applying knowledge and skill in mathematics to give some courses in mathematics and related field.
  5. Entrepreneur: applying the knowledge and skill in mathematical thinking framework related to creative business and entrepreneurship.
  6. Research assistant: applying mathematics and data processing knowledge and skill in research field.
  7. Further study in a doctoral program.

Program Educational Objectives

  1. Graduates with competence including analysis, algebra, applied mathematics and computing science
  2. Graduates who are able to compete internationally
  3. Graduates who are able to conduct research network related to applications of mathematics to real-life problems, mainly in the field of industry, information technology, energy, maritime, environment and finance
  4. Graduates who are able to develop their career, work more efficiently both individually or in a team, have better leadership and managerial capabilities

Program Learning Outcomes

  1. [C3] Students are able to solve mathematical problems by applying fundamental mathematical statements, methods, and computations
  2. [C4] Students are able to analyze mathematical problems in one of the fields: analysis, algebra, modeling, system, optimization or computing sciences
  3. [C5] Students are able to work and research collaboratively on mathematical problems within either the area of pure mathematics or applied mathematics or computing sciences
  4. Students are able to communicate and present mathematical ideas with clarity and coherence, both written and verbally
  5. Students are able to make use of the principles of long life learning to improve knowledge and current issues on mathematics
  6. Students are able to demonstrate religious attitude and tolerance
  7. Students are able to demonstrate an attitude of responsibility and commitment to law enforcement, ethics, norms for community and environmental sustainability


In the 2018 curriculum, the study load is 36 credits which are scheduled for 4 (four) semesters and a maximum of 8 (eight) semesters including the completion of a thesis (Academic Regulations of ITS 2019). Compulsory courses consist of 4 courses (11 credits), namely Module Theory, Functional Analysis, Mathematical Modeling and Numerical Computation.

The study load of the master program is at least 36 credits and at most 50 credits are scheduled for 4 (four) semesters and can be taken in less than 4 (four) semesters and for a maximum of 10 (ten) semesters including thesis, after an undergraduate program, or equivalent (Kepmendiknas No. 232 / U / 2000).

The study load that must be taken to complete studies at PSMM-ITS is 36 credits, with a period of 4 semesters.

All courses are grouped as follows:

  • Compulsory courses: Module Theory (3 credits), Functional Analysis (3 credits), Mathematical Modeling (3 credits) and Numerical Computation (2 credits).
  • The compulsory courses for each group is 6 credits: Approximate Theory (3 credits), Max-Plus Algebra (3 credits), Dynamic Systems (3 credits), Stochastic Calculus (3 credits), Computational Algorithms (3 credits), Mathematics of Machine Learning (3 credits).
  • Elective courses in the Applied Analysis and Algebra group: Discrete Transformation (3 credits), Graph Algebra (3 credits), Wavelets and Applications (3 credits), Graphs and Applications (2 credits), Topics in Applied Analysis (2 credits), Topics in Applied Algebra (2 credits) .
  • Elective courses in Modeling and Simulation group: Systems and Control (3 credits), Computational Fluid Dynamics (3 credits), Dynamic Optimization (3 credits), Financial Mathematics (3 credits), Biological Mathematics (3 credits), Data Assimilation (3 credits), Derivative Mathematics (3 credits), Risk Analysis (3 credits), Advanced Partial Differential Equations (2 credits), Inverse Problems (2 credits), Topics in Mathematical Modeling (2 credits), Topics in Optimization (2 credits).
  • Elective courses in Computer Science group: Formal Verification (3 credits), Digital Image Processing and Analysis (3 credits), Computational Biology (3 credits), Theory of Computations (3 credits), Fuzzy Systems (2 credits), Topics in Computations (2 credits).


The minimum study load of students in MoMath is 36 SKS, which is equivalent to 170 minutes self study and attendance-based learning every week. In every semester, there are 16 weeks of lecture or other scheduled activities, including evaluation. The study load and other required activities are equivalent to 99.96 ECTS, where 1 ECTS equals 25 working hours. The other required activities (outside the study load) are:

  1. Some elective courses in Semester 2 and 3 requires the students to work on a specific problem, write the results in a technical report and present the results in an internal seminar. The activities are equivalent to 3 SKS.
  2. As a requirement for graduation, each student must publish some results of the thesis to an international conference or a journal. The activities are preparing the paper, writing the paper, revising the paper, preparing the camera-ready version, presenting the paper. The activities are equivalent to 5 SKS.
  3. There are some activities in thesis, which take some time, such as writing the proposal, presenting the proposal, revising the proposal, presenting the results in an internal seminar. The activities are equivalent to 5 SKS.

The conversion table between the minimum study load and other required activities, from SKS to ECTS is described in the following table:

Components SKS ECTS
Minimum study load 36 73.44
Activities in some elective courses 3 6.12
Publication in conference or journal 5 10.20
Some activities in thesis 5 10.20
Total 99.96

List of Courses

No. Course Code Course Name Credit Download
1 KM185101 Module Theory 3 Syllabus  Module
2 KM185102 Functional Analysis 3 Syllabus  Module
3 KM185103 Mathematical Modeling 3 Syllabus  Module
4 KM185104 Numerical Computing 2 Syllabus  Module
Total credits 11
No. Course Code Course Name Credit
1 KM1852xx Compulsary Courses 6
2 KM1852xx Elective Courses 3
Total credits 9
No. Course Code Course Name Credit
1 KM1853xx Elective Courses 8
Total credits 8
No. Course Code Course Name Credit Download
1 KM185401 Thesis 8 Syllabus Module
Total credits 8
No. Code Compulsary Courses Credits Download
1 KM185211 Approximation Theory 3 Syllabus Module
2 KM185212 Max-Plus Algebra 3 Syllabus Module
3 KM185221 Dynamical Systems 3 Syllabus Module
4 KM185222 Stochastics Calculus 3 Syllabus  Module
5 KM185231 Computational Algorithm 3 Syllabus  Module
6 KM185232 Mathematics of Machine Learning 3 Syllabus  Module
No. Code Elective Courses Credits Download
1 KM185271 Discrete Transformation 3 Syllabus Module
2 KM185272 Formal Verification 3 Syllabus Module
3 KM185273 Systems and Controls 3 Syllabus  Module
4 KM185274 Computational Fluid Dynamics 3 Syllabus  Module
5 KM185275 Dynamical Optimization 3 Syllabus Module
6 KM185276 Financial Mathematics 3 Syllabus Module
7 KM185277 Digital Image Processing and Analysis 3 Syllabus  Module


No. Code Elective Courses  Credits Download
1 KM185372 Mathematical Biology 3 Syllabus  Module
2 KM185373 Data Assimilation 3 Syllabus  Module
3 KM185374 Computational Biology 3 Syllabus  Module
4 KM185375 Mathematics of Derivatives 3 Syllabus  Module
5 KM185376 Risk Analysis 3 Syllabus Module
6 KM185377 Graph Algebra 3 Syllabus Module
7 KM185378 Theory of Computing 3 Syllabus Module
8 KM185379 Wavelet and Applications 3 Syllabus Module
9 KM185380 Advanced Partial Differential Equations 2 Syllabus Module
10 KM185381 Inverse Problems 2 Syllabus Module
11 KM185382 Fuzzy Systems 2 Syllabus Module
12 KM185383 Graph and Applications 2 Syllabus Module
13 KM185384 Topics of Applied Analysis 2 Syllabus Module
14 KM185385 Topics of Computing 2 Syllabus Module
15 KM185386 Topics of Mathematical Modeling 2 Syllabus Module
16 KM185387 Topics of Applied Algebra 2 Syllabus Module
17 KM185388 Topics of Optimization 2 Syllabus Module

Samples of Portfolio

1 Data Assimilation Download
2 Biological Computing Download
3 Numerical Computing Download
4 Dynamical Optimization Download
5 Stochastic Calculus Download
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