The following are important components of the Computer Science study program curriculum:
The Study Program of Computer Science,FPMIPA,UPI was established with decree number of 1342/J33/PP.03.02/2005 on March 16, 2005and began to offer courses on July 17, 2005.It has received accreditation A (excellent) from the National Accreditation Board for Higher Education (BAN PT No. 4850/SK/BAN-PT/Akred/VIII/2020). As seen in the 2018 Curriculum structure, the Computer Science Study Program focuses on several areas of study and expertise, namely:(i) Software Engineering; (ii) Robotics Control, Artificial Intelligence, and Internet of Thing (IoT); (iii) Multimedia and Design; (iv) Computer Engineering and Network Systems; (v) Big Data Analysis; and (vi) Information Systems. In the 2018 Curriculum structure, all fields of study and expertise are represented in 6 packages of elective courses.
Based on the Decree of the Minister of Research, Technology and Higher Education Number 57/M/KPT/2019 of 2019 about the Name of Study Programs in Higher Education, the titles of the degree program for study programs of Computer Science is "S.Kom."
Based on lecturer discussion on several focus group discussions, a survey of alumni, and expectations of students as well as consideration of the results of discussions at the professional association of each study program, the following are the qualification profiles of study program of Computer Science:
The following are the Program Learning Outcomes of Computer Science:
UPI rector's regulation number 52/UN40/HK/2019 regarding Guidelines for the Implementation of Education at Universitas Pendidikan Indonesia of 2020, outlines that the study load of undergraduate program is between 144 and152 credits (or SKS, which is a credit system in Indonesia). In general, the curriculum structure of the undergraduate program consists of the core curriculum, which aims to develop the main competencies of graduates (which amounts to about 85% of the total credits can be taken by students) and the elective curriculum, which aims to strengthen the main competencies (which amounts to about 15% of the total credits can be taken by students). In detail, the curriculum structures of the undergraduate program are grouped into General Courses (MKU), Basic Education Courses (MKDK), Faculty Expertise Courses (MKKF), Field of Study Learning Courses (MKKPBS), Core Expertise Competencies Courses (MKIPS), and Study Program Elective Expertise Courses (MKKPS)
Table Group of Expertise in Computer Science Study Program
Computer Science Study Program |
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1. Software Engineering |
2. Computer Engineering and Network Systems |
3. Multimedia and Design |
4. Artificial Intelligence Robotics Control and IoT |
5. Big Data Analysis |
6. Information Systems |
The placement of study program courses in each semester is arranged in such a way so that the courses in particular semester which are prerequisite for courses of following semester canbe taken. This arrangement is expected to support student needs so that they will be able to master appropriately and comprehensively the material presented in all courses in order to achieve all determined PLO. It is also expected that the students can complete their studies in accordance with the milestones of the curriculum.
The placement of study program courses in each semester is arranged in such a way so that the courses in particular semester which are prerequisite for courses of following semester canbe taken. This arrangement is expected to support student needs so that they will be able to master appropriately and comprehensively the material presented in all courses in order to achieve all determined PLO. It is also expected that the students can complete their studies in accordance with the milestones of the curriculum.
The placement of courses for each semester along with the linkages between courses that describe the breadth and depth of each course in Computer Science study program is presented in the Figures below:
Type of Course | Credit |
General Courses (MKU) | 14 SKS |
University Specificity Courses (MKKU) | 2 SKS |
Faculty Skill Courses (MKKF) | 6 SKS |
Core Expertise Courses of Study Program (MKKIPS) | 102 SKS |
Expertise Elective Of Study Programs Courses (MKKPS) | 9 SKS |
Field Experience Program Courses (MKPPL) | 4 SKS |
Expertise courses in the Computer Science Study Program are divided into 6 course packages or called Professional Competencies (KBK). The six KBKs are Software Engineering, Artificial Intelligence and IoT Robotics Control, Multimedia and Design, Computer Engineering and Network Systems, Big Data Analysis, and Information Systems.
The following table is a complete list of courses in the Computer Science Study Program. The handbook modules can be accessed by pressing the name of each course or you can download the module file here.
NO | COURSE CODE |
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1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||||
1 | KU100 | Islamic Education |
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X | |||||||
2 | KU101 | Protestant Education |
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X | |||||||
3 | KU102 | Catholic Education |
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X | |||||||
4 | KU103 | Hindu Education |
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X | |||||||
5 | KU104 | Buddhist Education |
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X | |||||||
6 | KU109 | Khonghucu Education |
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X | |||||||
7 | KU110 | Pancasila |
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X | |||||||
8 | KU105 | Civic Education |
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X | |||||||
9 | KU106 | Indonesian Language |
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X | |||||||
10 | KU108 | Physical Education* |
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X | |||||||
11 | KU119 | Art Education* |
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X | |||||||
12 | KU300 | Seminar for Islamic Education |
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X | |||||||
13 | KU301 | Seminar for Protestan Education |
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X | |||||||
14 | KU302 | Seminar for Catholic Education |
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X | |||||||
15 | KU303 | Seminar for Hindu Education |
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X | |||||||
16 | KU304 | Seminar for Buddhist Education |
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X | |||||||
17 | KU309 | Seminar for Khonghucu Education |
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X | |||||||
18 | KU400 | Community Service Program /Student Study Service |
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X | |||||||
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NO | COURSE CODE |
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1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||||
1 | HU300 | Introduction of Education |
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X | |||||||
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NO | COURSE CODE |
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1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||||
1 | MA100 | Mathematics, Science, Technology and Engineering |
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X | |||||||
2 | MA200 | Applied Mathematics, Science, Technology and Engineering |
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X | |||||||
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NO | COURSE CODE |
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1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||||
1 | IK500 | Machine Learning |
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X | |||||||
2 | IK510 | Parallel and Distributed Computing |
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X | |||||||
3 | IK520 | Project Expertise |
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X | |||||||
4 | IK530 | Mobile Application Development |
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X | |||||||
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NO | COURSE CODE |
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1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||||
1 | IK501 | Testing and Software Management |
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X | |||||||
2 | IK511 | Applied Marine Engineering |
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X | |||||||
3 | IK521 | Service Computing Engineering |
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X | |||||||
4 | IK531 | Game Application Development |
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X | |||||||
5 | IK541 | Interfacing Technique |
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X | |||||||
6 | IK551 | Software Quality Management |
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X | |||||||
7 | IK561 | Business Application Engineering |
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X | |||||||
8 | IK571 | Information Engineering |
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X | |||||||
9 | IK581 | Software Quality Assurance |
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X | |||||||
10 | IK591 | Compilation Techniques |
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X | |||||||
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NO | COURSE CODE |
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1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||||
1 | IK502 | Digital Image Processing |
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X | |||||||
2 | IK512 | Intelligent Games |
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X | |||||||
3 | IK522 | Natural Language Processing |
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X | |||||||
4 | IK532 | Deep Learning |
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X | |||||||
5 | IK542 | Computer Vision |
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X | |||||||
6 | IK552 | Internet of Things |
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X | |||||||
7 | IK562 | Control and Robotics |
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X | |||||||
8 | IK572 | Expert System |
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X | |||||||
9 | IK582 | Speech Recognition and Synthesis |
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X | |||||||
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NO | COURSE CODE |
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1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||||
1 | IK503 | Audio and Video Techniques |
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X | |||||||
2 | IK513 | Game Programming |
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X | |||||||
3 | IK523 | Visual Communication Design |
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X | |||||||
4 | IK533 | Audio and Video Manipulation |
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X | |||||||
5 | IK543 | Multimedia Production |
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X | |||||||
6 | IK553 | Social and Media Innovation |
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X | |||||||
7 | IK563 | Animation Techniques |
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X | |||||||
8 | IK573 | Open Distance Learning |
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X | |||||||
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NO | COURSE CODE |
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1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||||
1 | IK504 | Mobile Networking |
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X | |||||||
2 | IK514 | Cloud Technology |
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X | |||||||
3 | IK524 | Network Administration |
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X | |||||||
4 | IK534 | Wireless Technology |
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X | |||||||
5 | IK544 | Computer Forensics |
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X | |||||||
6 | IK554 | Telecommunications Network Design |
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X | |||||||
7 | IK564 | Information System Security |
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X | |||||||
8 | IK574 | Advanced Computer Network |
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X | |||||||
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NO | COURSE CODE |
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1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||||
1 | IK505 | Data Mining and Warehouse |
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X | |||||||
2 | IK515 | Computational Statistics |
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X | |||||||
3 | IK525 | Decision Support System |
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X | |||||||
4 | IK535 | Data Visualization |
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X | |||||||
5 | IK545 | Big Data Platforms |
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X | |||||||
6 | IK555 | Data Analysis |
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X | |||||||
7 | IK565 | Time Series Data Analysis |
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X | |||||||
8 | IK575 | Data Management |
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X | |||||||
9 | IK585 | Financial Technology |
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X | |||||||
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NO | COURSE CODE |
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1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||||
1 | IK506 | Information Technology Strategic Planning |
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X | |||||||
2 | IK516 | Architecture and Integration Enterprise Application |
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X | |||||||
3 | IK526 | Information System for Accounting |
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X | |||||||
4 | IK536 | Information System for Education |
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X | |||||||
5 | IK546 | Information System Audit |
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X | |||||||
6 | IK556 | IT Infrastructure and Emerging Trends |
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X | |||||||
7 | IK566 | Business Intelligence |
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X | |||||||
8 | IK576 | Application Systems for Business Functions |
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X | |||||||
9 | IK586 | Geographic Information System |
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X | |||||||
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NO | COURSE CODE |
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1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||||
1 | IK590 | Internship |
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X | |||||||
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To achieve optimal program learning outcomes, each study program applies student-centered learning approachin its learning models, methods and strategies. This approach aims to encourage all students to have the ability to acquire knowledge for the future long-run, create new understandings, solve problems, do research, and communicate knowledge to others. Theapproaches, models, methods, and strategies have been agreed upon by all lecturers. Meanwhile, the head of the study program regularly controls the teaching and learning process through the Quality Control Task Force (GKM). The task of GKM is to monitor the teaching and learning process.
Teaching and learning strategies are applied through various methods and approaches. The use of the teaching and learning methods and approaches depends on the formulation of the Learning Outcome Program (PLO), course characteristics, as described in the module description, didactic and pedagogical views, learning methods, and the importance of teaching and learning activities. The learning methods linked to learning outcomes for study program of Computer Science is presented in table below:
PLO Code |
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Lecture | Discussion | Debate | Symposium | Brainstorming | Laboratory’s work/Practice | Demonstration | Simulation | Field Practice | |
PLO-1 | √ | √ | √ | √ | |||||
PLO-2 | √ | √ | √ | √ | √ | √ | √ | √ | |
PLO-3 | √ | √ | √ | √ | √ | ||||
PLO-4 | √ | √ | √ | √ | √ | √ | √ | ||
PLO-5 | √ | √ | √ | √ | √ | √ | √ | ||
PLO-6 | √ | √ | √ | √ | √ | √ | |||
PLO-7 | √ | √ | √ | √ |
Other practicums that are held outside campus are Student Study Service Program (KKN) and Internship Program (PPLSP). For Student Study Service Program, students apply the knowledge and skills they have learned in their studies in the community, whileat the same time receive practical skills and new knowledge relevant to the theoretical knowledge they have learned . Student Study Service Program activities are done by students in groups consisting of students from different study program background to ensure theirdifferentknowledge can complement each other. Each group of Student Study Service Program is supervised by a lecturer in charge. An the end of the program, each group makes a report about the result of their Student Study Service Program and a scientific article that will be submitted to relevant journal to add to UPI scientific publications. Both are the basics things students need to do besides their individualand in group activities to receive their final score for Student Study Service Program.
In Internship Program (PPLSP), students have teachingpracticums in the school classrooms where they have their internshipprogram and supervised by a teacher in charge and a lecturer. Students who take Internship Programhavetheir practicums for approximately one semester or at least sixteen face-to-face meeting in the classroom. To ensure the effectiveness of the program, students who take Internship program are not allowed to take any other coursesfor the semester. At the end of the program, each student will have an exam for the Internship Program by teaching in front of students in the classroom. Internship Program report also need to be submitted by the students individually. Both exam and report are assessed by the teacher in charge and the lecturer. The final score of the Internship Programis determined by how actively involved the students are in the program andthe results of the examination and the grade of the report.
Field Practice Program (PPL) is a program for non-educationstudy program students to experience practical work in a public or private company for one semester. Studes who are taking Field Practice Program are not allowed to take any other coursesfor the semester. Students work ingroup or individuallyduring Field Practice Program in a company and supervised by two lecturers from the students’ study program and one supervisor from the company. At the end of Field Practice Program, students make individual report from the Field Practice Program activities and have an exam in the company by making a presentation in front of lecturers. The student’s active involvementand how well they do in his/her presentation presentation are the criteria to determine their final score for Field Practice Program.
Assessment is designed to measure the level of student’s achievement based on the learning process and the results against predetermined targets. An assessment is usually arranged in such a way to include all the expected learning achievement targets associated with one or a number of competencies covering the aspects of knowledge, skills, and/or attitudes.
The Computer Science study programs of FPMIPA UPI use the multi-component assessment concept in its assessment process to measure the successful course achievement implementation level and the students final results achievement. The knowledge aspect is measured using a written exam; aspects of skills are measured using project tasks, performance, and/or presentations; and the attitude aspect is measured using peer assessment which is carried out in the activities of project assignments, work performance, and/or presentations. Based on the 2020 UPI Education Implementation Guidelines, the assessment of the student learning process is conducted at least twice in one semester, i.e. Mid-Semester Examination (UTS) and the Final Semester Examination (UAS).
Based on UPI Education Implementation Guidelines of 2020, 1 semester consists of 16 sessions with 14 sessions used for lectures, 1 session for the Mid-Semester Examination (UTS) and 1 session for the Final-Semester Examination (UAS). In general, the Mid-Semester Examination (UTS) is administered on the 8th session and the Final-Semester Examination (UAS) on the 16th meeting. The students can take the Final-Semester Examination (UAS) if they have attended at least 80% of the sessions in a particular course. The administration of Mid-Semester Examination (UTS) is left to the lecturer of the course and the cource schedule. The administration of the Final-Semester Examination (UAS), remedial teaching, and entry of grades is regulated by the Rector through he Vice Rector for Education and Student Affairs.
Mid-Semester Examination (UTS) and Final-Semester Examination (UAS) are organized according to the course categories, namely General Courses (MKU) and Core Expertise Competency Courses (MKIPS and MKPPS). This categorization is designed to help students take the exam in an orderly manner according to the specified time. The General Courses (MKU) exams are administered through a computer based test (CBT) by the General Courses (MKU) Study Program. The Core Expertise Competency Courses (MKIPS and MKPPS) examinations are conducted by the respective study programs. Examination schedules of the Core Competencey Courses are prepared by the study program and published offline or online on UPI Academics. The examination schedules are prepared by considering the time and student’s load. Only a maximum of two examinations are scheduled per day per class.
During the Covid-19 pandemic, tests are aministered online using the (http://spot.upi.edu), SPADA (http://spada.upi.edu) and other applications/platforms such as https://quizizz.com, https://safeexambrowser.org which can be accessed from anywhere without the students having to to come to campus.
Students who for some reason cannot take Mid-Semester Examination (UTS) and/or Final-Semester Examination (UAS) must inform the lecturer prior to the exam time and submit supporting documentary evidence for their absence. Students will only be given the opportunity to take a make up examination if their absence is caused by: (a) an illness (proven by a doctor's certificate); (b) performing a task of representing the university in intra-curricular or extra-curricular activities outside campus; or (c) other legitimate and permissible reasons. Make up examinations are administered and scheduled by the lecturer in agreement with the student.. Assessment is carried out objectively and transparently, and in accordance with the regulation and procedures, and the results are informed to the students. Results are announced to provide feedback to the students on what they have done and achieved and to enourage those who have not made optimal efforts to improve themselves and obtain the better results in the future.
Students who have not met the course passing criteria will receive a short-term treatment of remedial teaching and their have their semester learning plan (RPS) evaluated learning process improvement. Students who get an E (fail) in a course must sign up for the course again in the following semester. The grade to be assigned to the students is the final grade they will receive in the current semester. This is stated in the 2020 UPI Education Implementation Guidelines.
The Internship Program(PPL) is a field activity for non-education sciencestudents. It iscarried out in collaboration with public and private external institutions.Students of Mathematics Study Program who take part in the Internship Program(PPL)are guided by two lecturers from the Study Program and aperson from the company where thestudents have their Internship Program(PPL). Internship)in the Computer Science Study Programstudents taking the Internship Program (PPL)is supervisedby a lecturer from the Study Program and aperson from the company where the Internship Program(PPL) is attended.
Passingand final grade of the Internship Program (PPL)is determined based on the accumulated scoreobtained from the supervisor,from the institution/companywhere the student is apprenticing,and from the supervising lectureraccording to the formulastatedin the PPL guidebook.
In addition to the Internship Program (PPL), the students also learn about the wider community environment through the Student Community Service (KKN)whose implementation is coordinated by the UPI Research and Community Service Institute (LPPM). The students can download information related to the Student Community Service (KKN) on the http://kkn.lppm.upi.edu/ page.
In the Student Community Service (KKN) students groups from various disciplinary backgrounds stay for a certain period of time in a community and participate actively in the life of the community. The group is guided by a supervising lecturer who serves as a companion and assessor for the student field activities. Before the Student Community Service (KKN) activities began, the lecturers and students would respectively be given briefing by the UPI Research and Community Service Institute (LPPM). Briefing for the supervisors relates to monitoring activities and information about the Student Community Service (KKN) assessments. Briefing for the students relates to the technical implementation of the Student Community Service (KKN) on the ground and the best practices. The supervising lecturer is responsible for monitoring activities. The assessment of the Student Community Service (KKN) participants is conducted on two aspects, namely the individual aspect and the group aspect. The supervising lecturer reports the KKN students score to the UPI Research and Community Service Institute (LPPM) for further entry into the system. During the COVID-19 pandemic, UPI made some adjustment to the theme to allow participants (students) to continue to participate in activities risking their health health and safety.
There are two tracks for completion of undergraduate (S1) study, namely the thesis track and the non-thesis track. The non-thesis track is for students who have a GPA (IPK) of 2.00 - 2.50. They are required to take 6 credits (SKS) of courses and make a thesis substitute paper. The study program makes regulations andSOPs for completion of study without a thesis track. The hesis track can be taken by students who have passed the thesis prerequisite courses, obtaining a minimum 105 credits (SKS) and GPA (IPK) of at least 2.50, and have received recommendation from the academic supervisor lecturer.
The procedures for thesis writing are as follows: thesis coordinator assigns supervisors I and II to the students; the students are guided in writing a thesis proposal; students take the thesis proposal examination. If the proposal approved by the examiners, students can continue the guidance process with the two supervisors, such as conducting activities to develop the instruments and collect data; proceed with writing the report of the data collection results, discussion, and conclusion. If the thesis writing results are considered appropriate by the supervisors, the student can take the thesis defense.
The proposal writing and examination,exam follows the SOP; the procedure for the thesis defense follows the SOP. The final score of the thesis includes the assessment of thesis writing and thesis defense examination. A student is said to have passed the thesis examination if his final-thesis score is at least 2.0 (on the scale of 4). The thesis research is carried out independently under the guidance of the two thesis supervisors. In general, the procedure for final completion of the study through the thesis track is presented in the following flowchart below.
Figure The flowchart of study completion procedures through thesis track in Computer Science Education Department
The assessment system in four study programs includes student learning processes and outcomes. This system includesformative and summativeassessments.Formative assessment aims to obtain information that can be used to improve the learning process (program delivery), while summative assessment is used to assess the achievement of student learning outcomes (learning outcomes). The assessment guidelines generally follow the UPI Education Implementation Guidelines which can be accessed at https://dit-akademik.upi.edu/.
Principle of Benchmarking Assessment (PAP) is used to assess student’s performance in a course. With PAP student achievement of a goal/competency is assessed against predetermined criteria . The final grade of a course is the cumulative result of the components of the assignment, mid-semester exams, final-semester exams, and other assessment components. In practice, each course has its own assessment procedures and techniques appropriate to its characteristics as long as it is not in conflict with the provisions of UPI Education Implementation Guidelines. This practical assessment guideline is contained in the course syllabus/RPS developed by a lecturer.
In general, each subject uses integrative assessment techniques, namely between the process and the results, and between other assessment components: attendance, quizzes, mid-semester exams (UTS), final-semester exams (UAS), assignments, practicum (if any), participation, and presentation (if any). The percentage of the assessment components include the element of attendance (5%); participation/quizzes/assignments/presentations (15%); practicum (10%); mid-semester exam (35%); and final-semester exams (35%). Table below is the illustrates guidelines for determining grades for each course.
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Grade | Value | Degree of Quality | ||
A | 4,0 | Excellent |
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A- | 3,7 | Almost Excellent |
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B+ | 3,4 | Very Good |
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B | 3,0 | Good |
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B- | 2,7 | Fairly Good |
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C+ | 2,4 | Very Satisfactory |
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C | 2,0 | Satisfactory |
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D | 1,0 | Poor |
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Minimum requirement for graduation |
E | <1,0 | Fail |
|
course should be retaken |
In compulsory courses, the minimum passing grade is C, while for elective courses the minimum passing grade is D. Students who get an E (fail), should retake the class in the following year in order to get the appropriate grade with pass criteria.
Students cannot cancel the grades of courses that they have passed, unless the they transfer to a different department/study program, where they have to cancel the expertise courses of the previous study program, or in the case where a student sign up for more than the maximum study load specified in the curriculum structure.
The achievement of the course learning outcome (CLO) can be seen from the student learning outcomes in these courses which are measured using summative test instruments. Each test instrument consists of a number of questions, each of which measures the CLO in that course. Each question that will be used for the exam is validated first by the head of the KBK where the subject is located. Parallel course (the same course taught in different classes) must use the same questions and assessment criteria. The final course outcomes are then evaluated by the GKM team to ensure the achievement of PLOs related to these courses. The results of the assessment in the form of the final grade must be inputted by the lecturer of the course into the lecturer score input system through the website https://siak.upi.edu/sinndo. If the lecturer is late in inputting the grades, the lecturer in question will receive a warning and consequences.
Students can view the final grade information for the courses they contact through the website https://student.upi.edu. Lecturers are obliged to design, manage, provide assignments/exams, provide feedback, and maintain student academic documents in an orderly manner. Relevant tothis obligation, students have the right to obtain clear and transparent information about their study results in the form of test scores, quizzes, assignments or projects, practicum, and/or presentations.
Students who have questions about their grades can directly contact the lecturer to discuss the problem. If there is an error in the process of inputting the grades, the lecturer can correct the grade to match the facts as long as it is still within the period of grade entry. If input error is detected after the grade entry period is over, the lecturer can submit a request letter to the UPI Academic Directorate to correct the grade.
The relationship between the learning outcome, the learning strategy, and the assessment used to measure the learning outcome in Computer Science study program will be presented below.
Table below, illustrates the relationship between PLO, learning strategies and assessment in Computer Science Study Program.
PLO Code | Teaching Method | Assessment |
---|---|---|
PLO-1 | Lectures, discussions, debates and brainstorms | Multiple choice |
PLO-2 | Lectures, discussions, symposiums, brainstorming, laboratory/practicum, demonstrations, simulations, field experiences | Presentations, Student project/projects, essay exams, oral exams, multiple choice exams, short answer exams |
PLO-3 | Lectures, discussions, laboratory/practicum, demonstrations, and simulations | Essay exams, verbal exams, multiple choice exams, and short answer exams |
PLO-4 | Lectures, discussions, brainstorming, laboratory/practicum, demonstrations, simulations, and field experiences | Student project/projects and practicum |
PLO-5 | Lectures, discussions, debates, symposiums, brainstorming, laboratory/practicum, and field experiences | Scientific articles |
PLO-6 | Lectures, discussions, laboratory/practicum, demonstrations, simulations, and field experiences | Scientific articles |
PLO-7 | Lectures, debates, symposiums and field experiences | Scientific articles and presentations |
The following table describes the mapping between Study Program Learning Outcomes (PLO) and the expected Graduate Qualifications:
PLO Code |
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|
|
|
|
PLO-1 | √ | √ | √ | √ | √ |
PLO-2 | √ | √ | |||
PLO-3 | √ | √ | √ | √ | √ |
PLO-4 | √ | √ | √ | ||
PLO-5 | √ | √ | |||
PLO-6 | √ | √ | √ | ||
PLO-7 | √ | √ | √ | √ | √ |
The following table describes the mapping between Study Program Learning Outcomes (PLO) and existing Study Program Courses:
NO | COURSE CODE |
|
|
|
|||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PLO-1 | PLO-2 | PLO-3 | PLO-4 | PLO-5 | PLO-6 | PLO-7 | |||||||
1 | KU100 | Islamic Education |
|
√ | |||||||||
2 | KU101 | Protestant Education |
|
√ | |||||||||
3 | KU102 | Catholic Education |
|
√ | |||||||||
4 | KU103 | Hindu Education |
|
√ | |||||||||
5 | KU104 | Buddhist Education |
|
√ | |||||||||
6 | KU109 | Khonghucu Education |
|
√ | |||||||||
7 | KU110 | Pancasila |
|
√ | √ | ||||||||
8 | KU105 | Civic Education |
|
√ | √ | ||||||||
9 | KU106 | Indonesian Language |
|
√ | |||||||||
10 | KU108 | Physical Education* |
|
√ | √ | ||||||||
11 | KU119 | Art Education* |
|
√ | √ | ||||||||
12 | KU300 | Seminar for Islamic Education |
|
√ | |||||||||
13 | KU301 | Seminar for Protestan Education |
|
√ | |||||||||
14 | KU302 | Seminar for Catholic Education |
|
√ | |||||||||
15 | KU303 | Seminar for Hindu Education |
|
√ | |||||||||
16 | KU304 | Seminar for Buddhist Education |
|
√ | |||||||||
17 | KU309 | Seminar for Khonghucu Education |
|
√ | |||||||||
18 | KU400 | Community Service Program /Student Study Service |
|
√ | √ | ||||||||
|
|
|
|
|
|
|
|
|
NO | COURSE CODE |
|
|
|
|||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PLO-1 | PLO-2 | PLO-3 | PLO-4 | PLO-5 | PLO-6 | PLO-7 | |||||||
1 | HU300 | Introduction of Education |
|
√ | √ | ||||||||
|
|
|
|
|
|
|
|
|
NO | COURSE CODE |
|
|
|
|||||||
---|---|---|---|---|---|---|---|---|---|---|---|
PLO-1 | PLO-2 | PLO-3 | PLO-4 | PLO-5 | PLO-6 | PLO-7 | |||||
1 | MA100 | Mathematics, Science, Technology and Engineering |
|
√ | |||||||
2 | MA101 | Applied Mathematics, Science, Technology and Engineering |
|
√ | |||||||
|
|
|
|
|
|
|
|
NO | COURSE CODE |
|
|
|
|||||||
---|---|---|---|---|---|---|---|---|---|---|---|
PLO-1 | PLO-2 | PLO-3 | PLO-4 | PLO-5 | PLO-6 | PLO-7 | |||||
1 | IK100 | Algorithm and Programming 1 |
|
√ | √ | √ | √ | ||||
2 | IK110 | Calculus |
|
√ | √ | ||||||
3 | IK120 | Programming Paradigm |
|
√ | √ | √ | |||||
4 | IK130 | Informatics Logic |
|
√ | √ | ||||||
5 | IK140 | English |
|
√ | √ | ||||||
6 | IK150 | Statistics |
|
√ | √ | ||||||
7 | IK160 | Algorithm and Programming 2 |
|
√ | √ | √ | √ | ||||
8 | IK170 | Database System |
|
√ | √ | √ | √ | √ | √ | ||
9 | IK180 | Linier Algebra and Metrics |
|
√ | √ | ||||||
10 | IK190 | Professional Ethics of Information and Communication Technology |
|
√ | √ | ||||||
11 | IK200 | Architecture and Organization Computer |
|
√ | √ | ||||||
12 | IK210 | Numerical Method |
|
√ | √ | √ | √ | √ | √ | ||
13 | IK220 | Control System |
|
√ | √ | ||||||
14 | IK230 | Design and Web Programming |
|
√ | √ | √ | √ | √ | √ | ||
15 | IK240 | Data Structure |
|
√ | √ | ||||||
16 | IK250 | Operating System |
|
√ | √ | √ | √ | √ | √ | ||
17 | IK260 | Automata Theory and Languages |
|
√ | √ | √ | |||||
18 | IK270 | Software Engineering |
|
√ | √ | ||||||
19 | IK280 | Artificial Intelligence |
|
√ | √ | √ | |||||
20 | IK290 | Design and Object-Oriented Programming |
|
√ | √ | √ | |||||
21 | IK207 | Computer Network |
|
√ | √ | √ | |||||
22 | IK217 | Information System |
|
√ | √ | √ | √ | √ | √ | ||
23 | IK227 | Techniques of Operations Research |
|
√ | √ | √ | |||||
24 | IK237 | Design and Analysis of Algorithms |
|
√ | √ | √ | |||||
25 | IK300 | Visual Programming and Mobile Device |
|
√ | √ | √ | |||||
26 | IK310 | Cryptography |
|
√ | √ | √ | √ | √ | √ | ||
27 | IK320 | Computer Graphics and Multimedia |
|
√ | √ | √ | |||||
28 | IK330 | Software Project Management |
|
√ | √ | √ | √ | √ | √ | ||
29 | IK340 | Intelligent system |
|
√ | √ | √ | |||||
30 | IK350 | Computer and Human Interaction |
|
√ | √ | √ | √ | √ | √ | ||
31 | IK360 | Capita Selecta |
|
√ | √ | √ | √ | √ | √ | ||
32 | IK370 | Simulation and Modeling Techniques |
|
√ | √ | ||||||
33 | IK380 | Non-Relational Databases |
|
√ | √ | √ | √ | √ | √ | ||
34 | IK400 | Research Methodology |
|
√ | √ | √ | √ | √ | √ | ||
35 | IK410 | Computer Science Entrepreneurship |
|
√ | √ | √ | |||||
36 | IK420 | Seminar |
|
√ | √ | √ | |||||
37 | IK430 | E-Business |
|
√ | √ | √ | √ | √ | √ | ||
38 | IK598 | Undergraduate Thesis |
|
√ | √ | √ | √ | √ | √ | √ | |
39 | IK599 | Thesis Defense |
|
√ | √ | √ | |||||
|
|
|
|
|
|
|
|
|
NO | COURSE CODE |
|
|
|
|||||||
---|---|---|---|---|---|---|---|---|---|---|---|
PLO-1 | PLO-2 | PLO-3 | PLO-4 | PLO-5 | PLO-6 | PLO-7 | |||||
1 | IK500 | Machine Learning |
|
√ | √ | √ | √ | √ | |||
2 | IK510 | Parallel and Distributed Computing |
|
√ | √ | √ | √ | √ | |||
3 | IK520 | Project Expertise |
|
√ | √ | ||||||
4 | IK530 | Mobile Application Development |
|
√ | √ | ||||||
|
|
|
|
|
|
|
|
|
NO | COURSE CODE |
|
|
|
|||||||
---|---|---|---|---|---|---|---|---|---|---|---|
PLO-1 | PLO-2 | PLO-3 | PLO-4 | PLO-5 | PLO-6 | PLO-7 | |||||
1 | IK501 | Testing and Software Management |
|
√ | √ | √ | √ | √ | |||
2 | IK511 | Applied Marine Engineering |
|
√ | √ | √ | √ | ||||
3 | IK521 | Service Computing Engineering |
|
√ | √ | √ | |||||
4 | IK531 | Game Application Development |
|
√ | √ | √ | √ | ||||
5 | IK541 | Interfacing Technique |
|
√ | √ | √ | |||||
6 | IK551 | Software Quality Management |
|
√ | √ | √ | √ | √ | |||
7 | IK561 | Business Application Engineering |
|
√ | √ | √ | |||||
8 | IK571 | Information Engineering |
|
√ | √ | √ | |||||
9 | IK581 | Software Quality Assurance |
|
√ | √ | √ | √ | √ | |||
10 | IK591 | Compilation Techniques |
|
√ | √ | √ | |||||
|
|
|
|
|
|
|
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|||
|
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|
|
|
|
|
|
|
NO | COURSE CODE |
|
|
|
|||||||
---|---|---|---|---|---|---|---|---|---|---|---|
PLO-1 | PLO-2 | PLO-3 | PLO-4 | PLO-5 | PLO-6 | PLO-7 | |||||
1 | IK502 | Digital Image Processing |
|
√ | √ | √ | √ | √ | √ | ||
2 | IK512 | Intelligent Games |
|
√ | √ | √ | √ | √ | √ | ||
3 | IK522 | Natural Language Processing |
|
√ | √ | √ | √ | √ | √ | ||
4 | IK532 | Deep Learning |
|
√ | √ | √ | √ | √ | √ | ||
5 | IK542 | Computer Vision |
|
√ | √ | √ | √ | √ | √ | ||
6 | IK552 | Internet of Things |
|
√ | √ | √ | √ | √ | √ | ||
7 | IK562 | Control and Robotics |
|
√ | √ | √ | √ | √ | √ | ||
8 | IK572 | Expert System |
|
√ | √ | √ | √ | √ | √ | ||
9 | IK582 | Speech Recognition and Synthesis |
|
√ | √ | √ | √ | √ | √ | ||
|
|
|
|
|
|
|
|
|
|||
|
|
|
|
|
|
|
|
|
NO | COURSE CODE |
|
|
|
|||||||
---|---|---|---|---|---|---|---|---|---|---|---|
PLO-1 | PLO-2 | PLO-3 | PLO-4 | PLO-5 | PLO-6 | PLO-7 | |||||
1 | IK503 | Audio and Video Techniques |
|
√ | √ | √ | √ | ||||
2 | IK513 | Game Programming |
|
√ | √ | √ | √ | ||||
3 | IK523 | Visual Communication Design |
|
√ | √ | √ | √ | √ | √ | ||
4 | IK533 | Audio and Video Manipulation |
|
√ | √ | √ | √ | ||||
5 | IK543 | Multimedia Production |
|
√ | √ | √ | √ | ||||
6 | IK553 | Social and Media Innovation |
|
√ | √ | √ | √ | √ | √ | ||
7 | IK563 | Animation Techniques |
|
√ | √ | √ | √ | ||||
8 | IK573 | Open Distance Learning |
|
√ | √ | √ | √ | √ | √ | ||
|
|
|
|
|
|
|
|
|
|||
|
|
|
|
|
|
|
|
|
NO | COURSE CODE |
|
|
|
|||||||
---|---|---|---|---|---|---|---|---|---|---|---|
PLO-1 | PLO-2 | PLO-3 | PLO-4 | PLO-5 | PLO-6 | PLO-7 | |||||
1 | IK504 | Mobile Networking |
|
√ | √ | √ | √ | ||||
2 | IK514 | Cloud Technology |
|
√ | √ | √ | √ | ||||
3 | IK524 | Network Administration |
|
√ | √ | √ | √ | √ | √ | ||
4 | IK534 | Wireless Technology |
|
√ | √ | √ | √ | ||||
5 | IK544 | Computer Forensics |
|
√ | √ | √ | √ | √ | √ | ||
6 | IK554 | Telecommunications Network Design |
|
√ | √ | √ | √ | √ | √ | ||
7 | IK564 | Information System Security |
|
√ | √ | √ | √ | √ | √ | ||
8 | IK574 | Advanced Computer Network |
|
√ | √ | √ | √ | √ | √ | ||
|
|
|
|
|
|
|
|
|
|||
|
|
|
|
|
|
|
|
|
NO | COURSE CODE |
|
|
|
|||||||
---|---|---|---|---|---|---|---|---|---|---|---|
PLO-1 | PLO-2 | PLO-3 | PLO-4 | PLO-5 | PLO-6 | PLO-7 | |||||
1 | IK505 | Data Mining and Warehouse |
|
√ | √ | √ | √ | ||||
2 | IK515 | Computational Statistics |
|
√ | √ | ||||||
3 | IK525 | Decision Support System |
|
√ | √ | ||||||
4 | IK535 | Data Visualization |
|
√ | √ | √ | |||||
5 | IK545 | Big Data Platforms |
|
√ | √ | √ | √ | ||||
6 | IK555 | Data Analysis |
|
√ | √ | √ | |||||
7 | IK565 | Time Series Data Analysis |
|
√ | √ | √ | √ | √ | √ | ||
8 | IK575 | Data Management |
|
√ | √ | √ | √ | ||||
9 | IK585 | Financial Technology |
|
√ | √ | √ | |||||
|
|
|
|
|
|
|
|
|
|||
|
|
|
|
|
|
|
|
|
NO | COURSE CODE |
|
|
|
|||||||
---|---|---|---|---|---|---|---|---|---|---|---|
PLO-1 | PLO-2 | PLO-3 | PLO-4 | PLO-5 | PLO-6 | PLO-7 | |||||
1 | IK506 | Information Technology Strategic Planning |
|
√ | √ | √ | √ | √ | |||
2 | IK516 | Architecture and Integration Enterprise Application |
|
√ | √ | √ | √ | √ | |||
3 | IK526 | Information System for Accounting |
|
√ | √ | √ | √ | √ | √ | ||
4 | IK536 | Information System for Education |
|
√ | √ | √ | √ | √ | √ | ||
5 | IK546 | Information System Audit |
|
√ | √ | √ | √ | √ | √ | ||
6 | IK556 | IT Infrastructure and Emerging Trends |
|
√ | √ | √ | √ | √ | √ | ||
7 | IK566 | Business Intelligence |
|
√ | √ | √ | √ | √ | |||
8 | IK576 | Application Systems for Business Functions |
|
√ | √ | √ | √ | √ | |||
9 | IK586 | Geographic Information System |
|
√ | √ | √ | √ | √ | |||
|
|
|
|
|
|
|
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|||
|
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|
NO | COURSE CODE |
|
|
|
||||||
---|---|---|---|---|---|---|---|---|---|---|
PLO-1 | PLO-2 | PLO-3 | PLO-4 | PLO-5 | PLO-6 | PLO-7 | ||||
1 | IK590 | Internship |
|
√ | √ | √ | √ | |||
|
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|