Implementasi Teknik Data Mining untuk Evaluasi Kinerja Mahasiswa Berdasarkan Data Akademik - Gita Indah Marthasari (Universitas Darussalam Gontor, 2017)
One indicator of college efficiency is the study period of the students. It is important for university managers to improve the ratio of students who graduate on time. This research aims to analyze the characteristics that affect the study period of students from academic data. The methods used are association rule mining (ARM) and clustering. We propose a framework to analyze academic data using ARM dan clustering method. ARM method is a method to find association rules that meet minimum support and minimum confidence. The algorithm used is Apriori. While clustering using Simple Expectation-Maximization (EM-clustering) algorithm. Simple EM is a model-based algorithm that searches for maximum likelihood estimation in the probability model. The variables analyzed were student achievement index, province of the students, and type of high school. The analysis is done using WEKA. Research begins with the collection of data from the primary source of the Biro Administrasi Akademik (BAA) Universitas Muhammadiyah Malang (UMM). Then, we do the data cleaning and transformation. Analyzing process is done in two step. First, do a rule search using Apriori algorithm. The regulated parameters are the minimum support and minimum confidence value. Second, we use Simple EM algorithm for the clustering process. The experiments were conducted to find the clustering result with the largest log likelihood value. Based on the experiment, the method used successfully describes the characteristics based on the study period.