ACADSTAFF UGM
| Title | : | Elective Courses Recommendation System Using Genetic Algorithm |
| Author | : |
MUHAMMAD MUKHTAR K (1) Aina Musdholifah, S.Kom., M.Kom. Ph.D (2) |
| Date | : | 5 2021 |
| Keyword | : | Recommendation system,elective courses,genetic algorithm Recommendation system,elective courses,genetic algorithm |
| Abstract | : | A student in a second year and above has to choose many mandatory and elective courses. Each study program has many different elective courses. Elective courses are usually chosen based on the student’s interests and abilities as well as the experiences of other students. This research focuses on building a web-based elective courses recommendation system using genetic algorithms. The genetic algorithm is utilized to filter all the elective courses into ten elective courses. The set of ten candidates of recommended elective courses is considered as a chromosome of an individual. The recommended elective courses are given based on user profiles. The user profile consists of three factors, i.e., interest in courses, student’s grades, and student’s research interest. The fitness function of an individual is defined as the sum of all scores for all recommendation factors. This research used Curriculum 2016 of the Computer Science Bachelor Program for validation purposes. The proposed recommendation system is evaluated in two ways, i.e., questionnaire method and validation method. The questionnaire method obtains an assessment of system performance, hence the validation method to get the average accuracy. The questionnaire was conducted by involving thirty students of the CSUGM undergraduate program. The experimental results show that the proposed recommendation system has a good performance proven by the percentage of recommendation relevancy on 4.67 of 5. Furthermore, the accuracy of the proposed recommendation system has an average of 74.08 %. |
| Group of Knowledge | : | |
| Level | : | Internasional |
| Status | : |
Published
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