<![CDATA[In education, student performance is an important part. To achieve good and quality student performance requires analysis or evaluation offactors that influence student performance. The method still using an evaluation based only on the educator's assessment of information on theprogress 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 is79.31. 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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