Apriori Implementation to Find the Association Rules of the New Student Admission Data of STMIK AMIKOM Surakarta

Abstract

 The change in form from an academy to a school of Information and Computer Management allows STMIK AMIKOM Surakarta to accept new undergraduate students. One way to find out the characteristics of prospective students is to study the previous data on new student admission data. The research conducted is the implementation of data mining using association rules with the Apriori algorithm on registration data for new students at STMIK AMIKOM Surakarta. The reason for using association rules in this study is the data used even though there are classes that can be used for classification, but the class that appears in the data is still dominated by one class. The stages of this research start from the study of literature, the general process of data mining, and the analysis and evaluation of the established association rules. The results of the implementation of the Apriori algorithm to find association rules in the new student admissions data of STMIK AMIKOM Surakarta there are 23 rules that meet a minimum support of 0.3 and a minimum confidence of 0.9 which consists of a minimum of 3 antecedents. Out of the 13 attributes used as antecedent candidates, only 6 make up the a priori association-classification rules, namely gender, religion, province, type of school, study program, and registeration status.