<![CDATA[Student graduation is an important aspect of accreditation at a university. One of the problems of concern is that the number of new students is not balanced with the number of graduates (graduating on time) the problem can lead to potential dropouts. From these problems it is necessary to conduct an analysis to predict the study period at Halmahera University. The main objective of this research is to predict student study period using Naïve Bayes algorithm, by utilising student graduation data from 2014-2017. The approach used involves the stages of data selection, data preprocessing, analytical processing, and output, with evaluation of prediction results using confusion matrix. Prediction of study period is done by data mining method and using Naïve Bayes algorithm to find patterns (knowledge). The data used is student graduation data in 2014-2017 with a total of 1,157 records, the data is divided into 926 records for training data (80%) and 231 records as testing data (20%). The prediction results show the accuracy rate of the Naïve Bayes Algorithm is 82.9%.]]>
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