Module Mapping to PLOs for Undergraduate Program in Computer Science

Overview

Courses are mapped based on the Program Learning Outcomes and the graduate profile that aims to ease the measurement of the graduate outcomes. As an example of this mapping, the Pancasila course has graduate learning outcomes to support attitudes and values with strong values and professionalism with strong values as well. The conformity for the Pancasila course is conformed with all profile types. The complete mapping of each course with each of the graduate learning outcome and the graduate profile of the Undergraduate Program in Computer Science is shown in the table below. This course mapping was designed by taking into account the expected level of understanding strength possessed by the students, which are divided into three levels, namely Strong (S), Medium (M), and Weak (W).

Compulsory Courses
NOCourse NameProgram Learning Outcome*Graduate Profile **
PLO1PLO2PLO3PLO4PLO5CSAIEDSSECEDE
COMPULSORY COURSE
1ReligionSWMvvvvvv
2Calculus ISMWvvvvvv
3Basic Physics IMWvvvvvv
4Basic Chemistry IMWvvvvvv
5ProgrammingSSMWvvvvvv
6Lab Work in ProgrammingWSSMWvvvvvv
7Logic for Computer ScienceSMMWvvvvvv
8Elementary Linear AlgebraSMWvvvvvv
9Scientific Writing and EthicsSSMMMvvvvvv
10Integral and Differential EquationsMMWvvvvvv
11Introduction to StatisticsMMMvvvvvv
12Organization and Computer ArchitectureSMWvvvvvv
13<=”” td=”” style=”box-sizing: border-box;”>SSMWvvvvvv
14Lab Work in Algorithms and Data StructuresWSSMWvvvvvv
15Discrete MathematicsSSMvvvvvv
16Digital SystemsMMvvvvvv
17EnglishMMMSvvvvvv
18PancasilaSMSvvvvvv
19Probability and Stochastic ProcessesSMWvvvvvv
20Artificial IntelligenceSSMWvv
21Analysis of Algorithms and ComplexitySMWWvvvvvv
22Computer NetworksSMWWvv
23Operating SystemsSMWWvvvvvv
24Computer Systems and Networks Lab WorkWSMWWvv
25Computational ThinkingWMMSvvvvvv
26DatabaseSSMMvvvv
27Lab Work in DatabaseSSMMvvvv
28CitizenshipSWWMvvvvvv
29Numerical MethodsMMWWvvvvvv
30Software Engineering MethodsSSMMvvv
31Workshop on Implementing Software DesignWSSMMvvv
32Cryptography and Network SecuritySMWWvvvvvv
33Languages and AutomataMMWvvvvvv
34Startup Digital DevelopmentMMMMMvvvvvv
35Philosophy of Computer ScienceMWWvvvvvv
36Machine LearningMSSMvvv
37Deep LearningMSSMvvv
38Research Method of Computer ScienceMSMMMvvvvvv
39Seminar ClassWMMMWvvvvvv
40Software Engineering ProjectWMSSMvvv
41Community Development ParticipationMWMMvvvvvv
42Undergraduate Thesis ProposalMMSSMvvvvvv
43Undergraduate ThesisMMSSMvvvvvv

* S =Strong; M = Medium; W= Weak ** Graduate Profile: CS = Computer Scientist; AIE= Artificial Intelligence Engineer; DS=Data Scientist; SE= Software Engineer; CE= Cloud Engineer; DE= Digital Entrepreneur. *** MBKM Courses are to recognize up to the 20 loads of credit transfer if any of student plan taking the outside campus learning, or the industrial internships mobility which the related to Computer Science Profiles)

Elective Courses
NOCourse NameProgram Learning Outcome*Graduate Profile **
PLO1PLO2PLO3PLO4PLO5CSAIEDSSECEDE
ELECTIVE COURSES
Algorithms and Computational Labs
44Computer Vision and Image AnalysisMMWvv
45Optimization MethodsMMWWvv
46Distributed Algorithms and Parallel ProgrammingMMWWvvv
47Management ScienceWMWWvv
48Science SimulationWMMWvv
49Formal VerificationMMWv
50Digital Image ProcessingMMWvvv
51Computer GraphicsSMWv
52Research Trends on Algorithms and ComputationMMMMv
53Special Topic on Algorithm and ComputationMMMWv
Intelligent System Labs
54Natural Language ProcessingMSSMvv
55Fuzzy LogicSMSMv
56Decision Support SystemsSMMWvv
57Genetic AlgorithmsSMSMv
58Expert SystemsMMSMv
59BioinformaticsMSSWv
60Pattern RecognitionMMMvv
61Research Trend on Intelligent SystemsMMMMvvv
62Special Topic on Intelligent SystemsMMMWvvv
Software Engineering and Data Labs
63Front end and UI/UX DevelopmentMMMWvv
64Scalable Software DevelopmentMSSMvvv
65Introduction to Software Quality AssuranceMMMWvv
66Information Technology Project ManagementMMSMvv
67Data Mining and Business IntelligenceMMMWvvv
68Audit dan Digital ForensicsMMWvv
69Information RetrievalMMWv
70Semantic WebMMWvv
71Big Data AnalyticsMMWWvvvv
72Special Topics on Software and Data EngineeringMMMMvv
73Research Trends in Software and Data EngineeringMMMWvv
Computer System and Networking Labs
74Digital Society Network ModellingMMSMvvvvv
75High Performance Architecture and InfrastructureMMMWvv
76Mobile Application DevelopmentMSSMvvv
77Smart and Intelligent EnvironmentMMMvvv
78Cloud ComputingMMMWvv
79Big Data Architecture and InfrastructureMMMvv
80Special Topic on Computer and Network SystemsMMMWv
81Research Trends on Computer Systems and NetworksMMMWvv
82Internet of Things and ApplicationsMMMWvvvv
83Cyber System SecurityMMMWvvv
84Next Generation NetworksMMWvv
85Smart and Intelligent EnvironmentWMMSvvvv

* S =Strong; M = Medium; W= Weak ** Graduate Profile: CS = Computer Scientist; AIE= Artificial Intelligence Engineer; DS=Data Scientist; SE= Software Engineer; CE= Cloud Engineer; DE= Digital Entrepreneur. *** MBKM Courses are to recognize up to the 20 loads of credit transfer if any of student plan taking the outside campus learning, or the industrial internships mobility which the related to Computer Science Profiles).

Element #3