<![CDATA[The advancement of digital technology in education has driven the widespread adoption of Learning Management Systems (LMS) as effective platforms for online learning. This study aims to develop a predictive model for student success in LMS environments using machine learning algorithms. Student success is classified based on parameters such as participation levels, access frequency, assessment results, and punctuality in assignment submissions. Several machine learning algorithms, including Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbors, are employed to build the prediction model. The performance of each model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The results show that the Random Forest algorithm achieved the best performance with an accuracy of 89%, followed by Support Vector Machine and Decision Tree. The developed model is expected to assist educators and academic institutions in identifying students who may face learning difficulties at an early stage, allowing for timely and targeted interventions. This research contributes to the application of machine learning in supporting adaptive learning processes and enhancing data-driven educational quality.]]>
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