Penerapan Sistem Rekomendasi Registrasi Mata Kuliah Informatika UKDW
DOI:
https://doi.org/10.21460/jutei.2024.81.303Keywords:
Reccomendation, Courses, Item-Based Collaborative Filtering, Students, Informatics, Interface, Python, Laravel, TestcaseAbstract
Every time a new academic year begins, the UKDW Informatics study program will provide courses that students can choose when registering. The courses provided are basically divided into compulsory courses and elective courses. Based on these 2 types of courses, for Informatics students, especially in choosing elective courses, it is often adjusted to each student's area of interest. This area of interest is then adjusted to the courses available at the time of registration, however, Informatics students often experience confusion in choosing courses at the time of registration.
Therefore, this research will design a course recommendation system that is used to help recommend courses to UKDW Informatics Students before registering. There are 2 engines used by the system, namely Python and Laravel. Python is used to process recommendations, while Laravel is only used to display recommendations. The recommendation process carried out by Python will use Collaborative Filtering which has processes including collecting data in the form of course rating results, processing similarities between courses for each area of interest, and calculating an evaluation matrix using MAP. Then the results of the highest similarity will be used as criteria for displaying recommendations. The results of the recommendations will then be checked using Laravel, so that each student can have different course recommendations.
The system was tested with 10 test cases in the form of student scenarios from the classes of 2021, 2020 and 2019. The test case results show the courses that students should take, while the evaluation is related to the results of the courses recommended by the system. The testcase evaluation results were stated to have a success rate of 80% based on 8 out of 10 successful testcases. Furthermore, the system has an accuracy level for subjects of interest of 46.36% which is calculated using the Mean Average Precision (MAP) method by averaging based on the precision of 10 testcases. Precision is calculated by comparing the courses in the field of interest that have been recommended with the courses in the field of interest that have been taken in each test case.References
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Copyright (c) 2024 Renaldi Kristian Hartono, Aditya Wikan Mahastama, Yuan Lukito
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