<![CDATA[This study aims to predict students’ academic performance using the Naïve Bayes algorithm. The problem arises because academic assessment processes in many universities are still carried out manually, which can lead to subjectivity and inefficiency. Several factors—such as assignment scores, quizzes, examinations, attendance, motivation, and learning activities—significantly influence student performance, yet they have not been optimally utilized in prediction processes. The methods used in this research include data collection, preprocessing, splitting the dataset into training and testing sets, and applying the Naïve Bayes algorithm to classify student performance into categories of good, fair, and poor. The results indicate that the Naïve Bayes algorithm is capable of producing sufficiently accurate predictions and can be used as a decision-support tool to help improve the quality of learning in higher education institutions]]>
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