Implementasi Algoritma Apriori untuk Mendapatkan Pola Kelulusan Mahasiswa - Azwar Anas; Ade Jermawinsyah Zebua (LPPM Universitas Dinamika Bangsa, 2022)
Data transactions that occur every day will become chunks of data in an institution's database. These chunks of data have no value if mining is not carried out in obtaining interesting knowledge and information. The purpose of this study was to analyze chunks of graduation data for STIE-Graha Karya Muara Bulian students, in order to obtain patterns formed from predetermined variables. The method used is the Apriori Algorithm. This is in accordance with the main function of this algorithm, which is to analyze the frequent itemset. The results showed that for the 2-itemset data the highest support value was the combination of Female Gender and Work with a value of 34%, while the highest confidence value was in the combination of a Grade Point Average above 3.5 and Work with a score of 85%. As for the 3-itemset, the highest support value is in the combination if the GPA is above 3.5, gender is female and the work status reaches 14% and confidence is 90%.