STUDENT GRADUATION PREDICTION USING DECISION TREE ALGORITHM WITH CRISP-DM METHOD (CASE STUDY: ITB AHMAD DAHLAN) - Kholilah Husni; Elliya Sestri; Vany Terisia (Journal of Computer Science Advancements, 2025)
On-time graduation is an important indicator of higher education effectiveness; however, delays in student graduation are still observed at ITB Ahmad Dahlan Jakarta. This study develops a student graduation prediction system using the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology and the Decision Tree algorithm based on historical academic data. The model was built through six CRISP-DM stages, including problem understanding, data preparation, modeling, and evaluation. Testing results indicate high performance with an Accuracy of 97.44%, Precision of 97.14%, Recall of 100%, and F1-Score of 98.55%. This system has the potential to support strategic decision-making to enhance academic quality through data-driven approaches.