<![CDATA[The problem of student graduation in higher education is one of the most essential things in showing the quality of learning in higher education, especially on the Sumbawa University of Technology (UTS) campus. The purpose of this research is to compare three algorithm methods, namely C4.5, Naive Bayes, and K-Nearest Neighbor (KNN), which is better at predicting the timeliness of student graduation using RapidMiner tools with the Knowledge Discovery in Database (KDD) method. The dataset used by the three classifications is 330 Informatics student data. Based on the comparison of the three algorithms with data splitting techniques, it is found that the C4.5 algorithm produces an accuracy of 73.49% with a precision of 64.62% and a recall of 41.89%. The Naive Bayes algorithm produces an accuracy of 72.79% with a precision of 64.06% and a recall of 38.11%. Meanwhile, the K-Nearest Neighbor (KNN) algorithm produces an accuracy of 76.08% with a precision of 73.11% and a recall of 41.92%. From the comparison of the three algorithms, the most appropriate for predicting the timeliness of student graduation is the K-nearest neighbor (KNN) algorithm.]]>
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