<![CDATA[This research aims to develop a student academic monitoring website that integrates Machine Learning technology, with Collaborative Filtering and Decision Tree methods. This website is designed to improve the quality of education by providing effective tools for students, teachers, and parents in monitoring and analyzing academic performance. The main feature of the website is an interactive dashboard that displays students' grades, attendance, and extracurricular participation data in an easy-to-understand format. The implementation of a personalized academic recommendation system using the Collaborative Filtering method enables the provision of learning material suggestions that suit the individual needs of students. The Decision Tree method is used to classify academic progress, helping to identify areas that require improvement.]]>
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