Implementasi Algoritma FP-Growth untuk Menemukan Pola Keterkaitan Antara Matakuliah Pemrograman dan Matakuliah Matematika

Abstract

The specification of programming skills is one of the focuses of learning in the Informatics Engineering study program which requires students to understand and get good grades in all courses related to programming. The subject that is considered to have a relationship with the programming field is the Mathematics course. Efforts to determine the correlation between programming courses and mathematics courses through one of the association algorithms in data mining, namely the FP-Growth algorithm. FP-Growth was chosen because it has a faster data pattern execution rate than the a priori algorithm. The final stage of KDD produces 1227 data which is then processed using the FPGrowth algorithm. Tests with a minimum support value of 0.5 and minimum confidence of 0.7 show the same number of patterns between applications built with the SPMF application of 52250 patterns. The highest support value of 51% and the highest confidence value of 98% and the highest lift ratio value of 1.1941 in the combination of itemset patterns indicate that if students pass programming courses, then mathematics courses can also pass or vice versa.