Cross Department’s Elective Course Recommendation for University Students using Hybrid Filtering

The problem experienced by the average student is that it is challenging to choose the elective courses to be achieved because of the many elective courses offered. The solution that can be given is the existence of a recommendation system for choosing elective courses that can recommend classes according to the interests and abilities of the student. Each user will get different recommendations according to the profile and interests of the user. Although several studies have been conducted related to the recommendation system for electing electives, no research focuses on recommendations for taking elective courses across majors and predicting the value of the results of the procedures to be taken. This study focuses on the advice of elective classes and outcomes using Hybrid Filtering by combining collaborative filtering using the ID3 algorithm and content-based filtering Matrix. After implementation, we tested the accuracy by dividing the test data and training data differently—using academic data consisting of 1000 data. The accuracy result is 81and suggestions for further research can use evaluation of user satisfaction to see whether the results of the recommendations from the system are in the interests of students.