Module Handbook

 

Module Name:

Specialtopics in operations research and computation

Module Level:

Bachelor

Abbreviation, if applicable:

KST412

Sub-heading, if applicable:

-

Courses included in the

module, if applicable:

-

Semester/term:

7th/ FourthYear

Module coordinator(s):

Dr. Herry Suprajitno, M.Si.

Lecturer(s):

Drs. Edi Winarko M.Cs, Auli Damayanti M.Si, Asri Bekti Pertiwi M.Si.

Language:

BahasaIndonesia

Classification within the

curriculum

Compulsory Course/ Elective Studies

Teaching format / class

hours per week during semester:

2hours lectures (50 min / hour)

 

 

Workload:

2hours lectures, 2 hour structural activities, and 2 hours individual study, 13 week per semester, and total 78 hours per semester 2.6 ECTS.

 

 

 

Credit Points:

2

Requirements:

 

Learning goals/competencies:

General Competence (Knowledge)

Can prepare the undergraduate thesis proposal

Specific Competence:

1.       Using Genetic Algorithm (GA) to solve a problem

2.       Using Simulated Annealing (SA) to solve a problem

3.       Using Ant Colony Optimization (ACO) to solve a problem

4.       Using Particle Swarm Optimization (PSO) to solve a problem

5.       Using Artificial Bee Colony (ABC) to solve a problem

6.       Using Firefly Algorithm (FA) to solve a problem

7.       Using Data Mining Concepts to solve a problem

8.       Using Cryptography Concepts to solve a problem

9.       Using Firefly Algorithm (FA) to solve a problem

10.    Developing a proposal which continued into the undergraduated

Thesis

Content:

Genetic Algorithm, Simulated Annealing, Ant Colony Optimization, Particle Swarm Optimization, Artificial Bee Colony, Firefly Algorithm, Data Mining, Cryptography, Technique in preparing the undergraduate thesis proposal.

Soft skill Attribute

Discipline, honesty, teamworkand good communication

Study/exam achievements:

Students are considered to be competent and pass if at least get 40

of maximum mark of the exams (UTS dan UAS), structured activity

(group discussion).

Final score (NA) is calculated as follow: 20% assignment + 10% softskill + 10% quiz + 25% UTS + 35% UAS

 

Final index is defined as follow:

A : 75 100

AB : 70 - 74.99

B : 65 - 69.99

BC : 60 - 64.99

C : 55 - 59.99

D : 40 - 54.99

E : 0 - 39.99

Forms of Media:

LCD projectors, whiteboards

Learning Methods

Lecture, assessments, presentationand group discussion

Literature:

1.     Gen M dan Cheng R, 2000, Genetic Algorithms and Engineering Optimization, John Wiley & Sons, New York.

2.     Castro LN, 2006, Fundamentals of Natural Computing, Chapman & Hall, Boca Raton.

3.     Dorigo M dan Stutzle T, 2004, Ant Colony Optimization, MIT Press, Massachusetts

4.     Lazinica A, 2009, Particle Swarm Optimization, In-Tech, Vienna.

5.     Xin-She Yang, 2010, Firefly Algorithms for Multimodal Optimization, University of Cambridge.

6.     Karaboga D. dan Akay, B., 2009, A Comparative Study of Artificial Bee Colony algorithm, Applied Mathematics and Computation, 214, 108132

7.     Han J dan Kamber M, 2006, Data Mining Concepts and Techniques, 2nd edition, Elsevier, Oxford.

8.     Prasetyo E, 2012, Data Mining Konsep dan Aplikasi Menggunakan Matlab, Penerbit Andi, Yogyakarta.

9.     Stamp, M. dan Low, R M., 2007, Applied Cryptanalysis Breaking Ciphers in the Real World, John Wiley & Sons, New Jersey.

Notes: