Prediction Of Student Performance Using Decision Tree C 4.5 Algorithm - Rames Krisnan Kuntoro; Rukin Sudarwanto; Sriyanto - (Prosiding International conference on Information Technology and Business (ICITB), 2017)
This paper aims to make predictions of student achievement based on socioeconomic status of parents, student discipline and student achievement using data mining method with algorithm decision tree classifier C 4.5. For comparison, the research data were analyzed also with CHAID (Chi Squared Automatic Interaction Detection) and multiple regression. The research approach used is quantitative. The subject of this research is the elementary school students in SD Negeri 4 Trimulyo. Data collection techniques used are documented. The results of this study are very helpful for educational institutions to monitor the early improvement of student academic achievement, so that can be accompanied the learning process in order to achieve the expected performance Keywords: Data Mining, Classifier, Decision Tree, C45, CHAID, and Multiple Regression.