SISTEM REKOMENDASI PERENCANAAN STUDI MAHASISWA DENGAN MENGGUNAKAN ALGORITMA APRIORI DAN NAIVE BAYES (STUDI KASUS FTI UNTAR) - Elizabeth Erlsha; Lely Hiryanto (Jurnal Ilmu Komputer dan Sistem Informasi, 2015)
The system of student study plan recommendation is a system made using Apriori algorithm and Naive Bayes to create the recommendation of study plan for students in accordance with the maximum load of university credit unit (sks) and have a good chance of passing. The case study that is used in this system is Faculty of Information Technology at Tarumanagara University. Apriori algorithm is used to form a pattern of subjects formed into a frequent pattern tree (FP-tree). Naive Bayes is used to calculate the chances of recommendation passing, using the calculations of grade point average (IPK) and the maximum load of student university credit unit (sks). The system test results show that the system can provide one or more of the study plan recommendation. The percentage of similarity between study plan recommendation offered by the system with student academic record card may vary. This is caused by a list of subjects stored in the pattern of subjects may vary although the total load of stored university credit unit is the same and in fact, students often take subjects less than the maximum load of given university credit unit. Key wordsApriori,FakultasTeknologiInformasiUniversitasTarumanagara, Frequent Pattern Tree, Naive Bayes, Sistem Rekomendasi Perencanaan Studi.