Application of Data Mining using the Apriori Algorithm in Analyzing Subject Selection Patterns of Tutoring Students

Abstract

This study examines the application of data mining using the Apriori algorithm to analyze subject selection patterns among tutoring students in Kudus, Central Java. With the increasing number of students attending tutoring, understanding subject selection patterns is crucial to improve the effectiveness of educational services. The Apriori algorithm, a popular association rule mining technique, is used to identify relationships between frequently selected subjects. The research dataset consists of student subject selection transaction data, including information such as student name, student ID number, tutoring branch, and selected subjects. The analysis process included data preprocessing, data transformation into transaction format using Transaction Encoder, application of the Apriori algorithm with a minimum support of 0.05, and formation of association rules with a minimum confidence of 0.3. The results show frequent itemsets indicating the most popular subjects and association rules that describe students tendencies in selecting subject combinations. These findings can be utilized by tutoring managers to design more effective learning packages, optimize the allocation of teaching resources, and provide subject recommendations tailored to student needs. This research contributes to the development of educational data mining in the context of tutoring institutions in Indonesia.