<![CDATA[Improving the quality of education often requires a deep understanding of the factors that influence student performance. This research uses the Naïve Bayes algorithm to analyse data from the ‘Student Performance Factors’ dataset available on Kaggle to identify the influence of variables such as study time, attendance, and family support on student academic outcomes. The analysis process went through a preprocessing stage, including identifying missing values and converting numerical variables into categorical variables. The results show that the Naïve Bayes algorithm has a good ability to predict student performance, with a fairly high accuracy rate. Factors such as family support and study time intensity are the main indicators that influence the prediction results. Model evaluation was conducted using accuracy, precision and recall metrics, to ensure the reliability of the results. The conclusion of this research is that the Naïve Bayes algorithm can be effectively used to support the analysis of student performance in an educational context. By understanding the key factors that influence learning outcomes, the findings can be utilised to design more appropriate strategies to improve the quality of education.]]>
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