Comparison of Naïve Bayes and Random Forest Models in Predicting Undergraduate Study Duration Classification at the University of Lampung
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Abstract
This study aims to compare the performance of the Naïve Bayes and Random Forest classification algorithms in predicting the study duration of undergraduate students in the Mathematics Study Program at the University of Lampung. The dataset consists of 537 graduation records from 2020–2024. The research steps include data preprocessing, data partitioning (train-test split and k-fold cross validation), model building, and evaluation using a confusion matrix. The results show that the Random Forest algorithm achieved the highest accuracy of 94.44%, outperforming Naïve Bayes which reached a maximum accuracy of 92.59%. These findings suggest that Random Forest is more effective for classifying student study durations. These findings suggest that Random Forest is more effective for classifying student study durations.
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