Systematic Literature Review: Profiling Mahasiswa Menggunakan Metode Decision Tree

Abstract This study aims to systematically review the application of the Decision Tree method in student profiling activities using the Systematic Literature Review (SLR) approach. By analyzing 15 relevant scholarly articles, this research evaluates the techniques employed, the effectiveness of the Decision Tree method, and the most commonly used algorithms. The findings reveal that Decision Tree is one of the most widely used classification methods in education due to its ability to simplify decision-making processes and produce interpretable models. Algorithms such as ID3, C4.5, CART, and Random Forest are frequently applied in various studies, especially for academic performance prediction, dropout risk assessment, and student potential mapping. This study concludes that Decision Tree is an effective, efficient, and relevant method for supporting educational data analysis and evidence-based decision-making.