<![CDATA[One of the factors that determine the quality of higher education is the percentage of students' ability to complete their studies on time. Based on problems, a method is needed to predict student graduation on time. The purpose of the study is to determine the prediction of student graduation in the future. This research can generate new knowledge to help universities anticipate student graduations that are not on time. The method used is a data mining method with a naïve Bayes algorithm for classification. The attributes used are gender, parental income, length of guidance, working student status or not, semester 1 to semester 8 grades, and GPA. This research used Python 3 programming language and the Jupyter Notebook tool in Anaconda to process datasets. The distribution of datasets is divided by 70% for training data and 30% for testing data. The results of this study were obtained with the accuracy of the Naïve Bayes algorithm is 0.88. For the precision value, the on-time class has a value of 0.75 while late is 0.93. Based on the results of the research, the accuracy is enough to predict students' graduation on time.]]>
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