<![CDATA[Early identification of Student Study Status is essential for higher education institutions to implement proactive and strategic measures that facilitate timely completion of studies and mitigate dropout rates. This research intends to predict student study status with the Naïve Bayes method based on the features obtained from the implementation of Mutual Information. Feature selection through Mutual Information seeks to analyse the factors that most significantly impact the classification of student study status. The study status is categorized into three classes: dropout, enrolled, and graduate, based on 36 factors. The Mutual Information approach is employed to diminish data dimensions by discarding less relevant features while preserving critical information based on score values to achieve enhanced predictive accuracy. The selection of appropriate attributes enables the model to maintain simplicity while incorporating critical information aspects that significantly impact performance. Experiments were performed on a dataset comprising student academic variables, with data partitioning ratios of 80:20, 70:30, and 50:50 for training and testing datasets. The classification outcomes utilizing Naïve Bayes, without the use of Mutual Information across the three testing ratios, exhibited the accuracy of 68.29% in the 70:30 data split. Simultaneously, the classification outcomes utilizing Mutual Information across three test ratios are as follows: 71.64% accuracy at an 80:20 ratio with 10 selected attributes, 72.06% at a 70:30 ratio with 10 selected attributes, and the highest accuracy of 72.65% at a 50:50 ratio using 15 attributes. The utilization of the Naïve Bayes method for classifying student study status demonstrates enhanced accuracy when integrated with Mutual Information for feature selection. The findings of this study demonstrate that Mutual Information can streamline data by considering the quantity of attribute selections according to the ranking of their score values.]]>
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