Educational Data Mining in Predicting Student Final Grades on Standardized Indonesia Data Pokok Pendidikan Data Set

Abstract

Educational Data Mining has been implemented in predicting student final grade in Indonesia. It can be used to improve learning efficiency by paying more attention to students who are predicted to have low scores, but in practice it shows that each algorithm has a different performance depending on the attributes and data set used. This study uses Indonesian standardized students’ data named Data Pokok Pendidikan to predict the grades of junior high school students. Several prediction techniques of K-Nearest Neighbor, Naive Bayes, Decision Tree and Support Vector Machine are compared with implementation of parameter optimization and feature selection on each algorithm. Based on accuracy, precision, recall and F1-Score shows that various algorithm performs differently based on the high school data set, but in general Decision Tree with parameter optimization and feature selection outperform other classification algorithm with peak F1-Score at 61.48% and the most significant attribute in are First Semester Natural Science and First Semester Social Science score on predicting student final score.