<![CDATA[Student learning outcomes are important indicators for evaluating the success of the learning process and can be used to support data-driven academic decision-making. This study aims to compare the performance of the Random Forest and Gradient Boosting algorithms in predicting student learning outcomes. The study employed a quantitative approach with a comparative experimental design using the student dataset containing 500 student records and 11 attributes. The target variable was passed, while several attributes such as gender, age, study_hours_per_week, attendance_rate, parent_education, internet_access, extracurricular, and previous_score were used as predictors. The data preprocessing stage included data cleaning, missing value handling, categorical data transformation, feature selection, and train-test splitting with an 80:20 ratio. Model evaluation was conducted using confusion matrix, accuracy, precision, recall, F1-score, and ROC-AUC, supported by 10-fold cross validation. The results showed that Random Forest slightly outperformed Gradient Boosting on most evaluation metrics. On the test data, Random Forest achieved an accuracy of 87.00%, precision of 90.63%, recall of 89.23%, F1-score of 89.92%, and ROC-AUC of 93.27%, while Gradient Boosting obtained an accuracy of 86.00%, precision of 90.48%, recall of 87.69%, F1-score of 89.06%, and ROC-AUC of 93.10%. These findings indicate that both algorithms performed well in predicting student learning outcomes, with Random Forest showing more stable performance on the dataset used.]]>
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