<![CDATA[Student delays in completing their studies are experienced by most higher education institutions, for example at STMIK Widuri. STMIK Widuri must be able to predict student graduation early to prevent graduation that is not on time and maintain a good name and the accreditation assessment that has been obtained. For this reason, this research was conducted to predict the graduation of STMIK Widuri students using the classification method with the Naïve Bayes algorithm. Naïve Bayes is a classification algorithm that uses probability and statistics to predict a class. The dataset used is lecture activities of STMIK Widuri students class of 2021 from 2021-2022 odd to even 2022-2023 academic year and processed using the Rapidminer application. The dataset is processed through the stages of Knowledge Discovery in Database, including selection, pre-processing, transformation, data mining and evaluation stages. From the evaluation results using the confusion matrix on the distribution of training data 50% and data testing 50%, this study resulted in an Accuracy 93,10%, Precision 95,24%, and Recall 90%. In this way, it is hoped that STMIK Widuri can utilize attributes of the data stored in the database to be processed more optimally, for example using existing techniques in data mining.]]>
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