Data Mining Menggunakan Algoritma Decision Tree untuk Menentukan Kelulusan Mahasiswa dengan RapidMiner - Kevin April Akhmallahudin; Divia Cahyani; Dwiky Rachmatullah; Dzikrully Akbar; Hilmi Malik; Maulana Fansyuri (Journal of Information Technology and Informatics Engineering, 2025)
Student graduation prediction is an important aspect in higher education management which is intended to project students' chances of completing their studies on schedule. Accurate prediction results can support educational institutions in formulating strategic policies to improve the quality of academic services and provide more effective interventions and provide more effective support to students at risk of experiencing delayed graduation. This study applies the Decision Tree algorithm with the help of the RapidMiner application to build a student graduation prediction model, using data such as age, graduation status, and cumulative achievement index as the main variables. The results of the analysis show that the developed model is able to achieve a prediction accuracy level of 96.57%. This finding confirms that data mining techniques have great potential in helping educational institutions identify students who need special attention in order to complete their studies on time. Therefore, the results of this study not only play a role in the development of prediction models in the academic realm, but the results of this study can also be used as an initial basis for subsequent research that focuses on graduation prediction in the higher education environment.