期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：12
DOI：10.14569/IJACSA.2021.0121227
语种：Indonesian
出版社：Science and Information Society (SAI)
摘要：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.
