Students are one of the important pillars in the life cycle of a university. In the process of developing, a university can be influenced by how many bachelor degree (S1) graduates from the university are. The number of graduations of a college sometimes has a low ratio when compared to the number of students admitted in the same school year. This low passing rate of students can be caused by several factors, such as the number of student activities that are participated in, economic factors, and several other unexpected factors. This makes a university must have a scheme or a formula that can predict whether the student can graduate on time. Normally, a bachelor (S1) student takes 8 semesters of education. But the existence of several factors that have been mentioned can make the time to take S1 education to be more, or even fail to graduate. This study will try to compare the results of the analysis of the two methods in the classification algorithm to predict student graduation. The algorithm used is the K-Nearest Neighbor and Naïve Bayes Algorithm. This study also aims to identify the best algorithm among the two classification algorithm choices. This research concluded that the Naïve Bayes algorithm has the same level of accuracy as the KNN algorithm in predicting the graduation of students in the Medical Education study program, which is 90%

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