• Tyas Setiyorini STMIK Nusa Mandiri
  • Rizky Tri Asmono STMIK Swadharma
Keywords: Decision Tree, Naive Bayes, K-Nearest Neighbor, Student Performance


In education, student performance is an important part. To achieve good and quality student performance requires analysis or evaluation of
factors that influence student performance. The method still using an evaluation based only on the educator's assessment of information on the
progress of student learning. This method is not effective because information such as student learning progress is not enough to form indicators in evaluating student performance and helping students and educators to make improvements in learning and teaching. Previous studies have been conducted but it is not yet known which method is best in classifying student performance. In this study, the Decision Tree, Naive Bayes and K-Nearest Neighbor methods were compared using student performance datasets. By using the Decision Tree method, the accuracy is 78.85, using the Naive Bayes method, the accuracy is 77.69 and by using the K-Nearest Neighbor method, the accuracy is
79.31. After comparison the results show, by using the K-Nearest Neighbor method, the highest accuracy is obtained. It concluded that the KNearest Neighbor method had better performance than the Decision Tree and Naive Bayes methods


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How to Cite
Setiyorini, T., & Asmono, R. (2018). KOMPARASI METODE DECISION TREE, NAIVE BAYES DAN K-NEAREST NEIGHBOR PADA KLASIFIKASI KINERJA SISWA. Jurnal Techno Nusa Mandiri, 15(2), 85-92. https://doi.org/10.33480/techno.v15i2.16
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