Penerapan Algoritma FP-Growth Untuk Mengetahui Variabel Yang Memengaruhi Tingkat Kelulusan Mahasiswa FT UR

Abstract

Information is an important element in every fragment of human life that can be obtained in various possible ways. One of them is data mining. Data mining can form certain knowledge model and discover related patterns from sets of big data. Data mining application with association rules method and fp-growth algorithm can be utilized to discover variables that work on graduation rate. This study focuses on graduation, especially in the Engineering Faculty of Universitas Riau. Variables used in this study are the ways to get into university, the origin of student’s high schools, the high schools’ region, study majors, gender, age, time of the study, and GPA (Grade Point Average). The information displayed through the mining process is the value from support, confidence, and final of each variable tested. The result of this study is displayed on a web based and can be used by the dean and the head of study majors to cognize their student's graduation rate. Moreover, the result can be used for making a decision, such as quotation of student admission.
Keywords: Data Mining, Association Rules, FP-Growth, Graduation Rate, Web.