<![CDATA[This study applied the Naïve Bayes algorithm to predict student learning outcomes in the Basic Computer and Network Engineering subject at SMKN 1 Sipispis. A quantitative approach was employed, using data from 311 students, which consisted of both academic variables (assignments, midterm exams, and final exams) and non-academic variables (attendance, attitude, and learning interest). The dataset was preprocessed by cleaning, encoding, and splitting into training and testing sets using several ratios (90/10, 80/20, 70/30, and 60/40). The Naïve Bayes model was trained and evaluated using accuracy, precision, recall, and F1-score metrics. The best performance was achieved with the 80/20 data split, yielding an accuracy of 74.6%, demonstrating the model’s ability to capture probabilistic relationships between academic and non-academic factors. These findings indicate that the Naïve Bayes algorithm can effectively classify student performance levels such as Fair, Good, and Excellent, providing a reliable foundation for an automated decision support system. The developed web-based system can help teachers identify students at risk of declining performance early, enabling more adaptive and data-driven educational interventions]]>
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