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Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer
Published by Politeknik Negeri Banjarmasin
ISSN : 25983245     EISSN : 25983288     DOI : -
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
We are the Editor of Jurnal ELTIKOM, invites Mr. / Ms Lecturer, researcher and practitioner to be able to publish your paper on topics covering Electrical Engineering, Electronics Engineering, Telecommunications Engineering, Computer Engineering, Information Technology.
Arjuna Subject : -
Articles 11 Documents
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Search results for , issue "Vol. 6 No. 1 (2022)" : 11 Documents clear
Rekomendasi Pemilihan Mata Kuliah Dalam Pengisian Rencana Studi Mahasiswa dengan Penerapan Algoritma Apriori Syahrul, Ahmad; Solichin, Achmad
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 6 No. 1 (2022)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v6i1.549

Abstract

During the time of filling out the study plan, many Budi Luhur University students find it difficult to choose the courses to be taken in the next semester. Errors in the selection of courses can have an impact on the achievement of achievement that is not optimal, and can inhibit student graduation. The role of academic supervisors in helping students determine the taking of courses in the coming semester is very important, but in some cases, there are academic supervisors who do not have time to discuss directly with students. Therefore, in this study a system is proposed that can provide recommendations for students in taking courses when filling out study plans for the next semester. This course of recommendation making system uses the association rule mining method with a priori algorithm. The student's course history data is evaluated using an a priori algorithm to produce recommendations for choosing courses in the next semester. The trial results of this system show an average percentage of successful recommendations of 80.16% with a minimum confidence of 80%, and a minimum support of 7. With these results, this system can be used as a means of students to help make decisions in making courses at the coming semester.

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