Penerapan Association Rule dengan Algoritma Apriori pada Analisis Pemilihan Mata Kuliah Pilihan dan Bidang Keahlian pada Mahasiswa Teknik Industri UPN “Veteran” Jawa Timur

Abstract

The association rule technique is a nonparametric machine learning approach used to obtain hidden associations in databases between variables. This research analyzes student preference patterns and identifies relationships or associations related to elective courses chosen with the field of expertise or thesis topic to be chosen, and identifies the relationship between the choice of elective courses and the planned thesis field of expertise taken by students. Data is processed manually and using RapidMiner software. The first step taken is to form a combination of 1 itemset and calculate the support value, the stage is continued until the formation of a combination of 3 itemsets. From the association rules above, 1 pattern or rule (rules) is obtained that meets the minimum confidence of 80%. Since there is only 1 rule that meets the minimum confidence requirement, the rule becomes the final association. Based on the association that meets the conditions, it can be said that the majority of students have taken elective courses according to the plan for the thesis field of expertise to be taken.