Prediction of Hospital Administration Study Program Students' Graduation Using Decision Tree C4.5: Prediction of Hospital Administration Study Program Students' Graduation Using Decision Tree C4.5 - Samsinar, Samsinar; Sinaga, Rudolf; Afriany, Renny (STIKES Garuda Putih, 2025)
The timely graduation success of students is a crucial indicator in evaluating the quality of higher education institutions. This study aims to predict the graduation rate of undergraduate students in the Hospital Administration Program at STIKES Garuda Putih Jambi using the Decision Tree C4.5 algorithm. The data utilized includes the Cumulative Grade Point Average (CGPA) of 35 students over the first four semesters. The dataset was processed using RapidMiner to generate a prediction model with CGPA as the main variable. The model evaluation indicated an accuracy level of 73.33%. This classification model successfully categorized student graduation outcomes into three groups: satisfactory, very satisfactory, and with distinction. The findings of this study are expected to provide insights for better academic decision-making, as well as enhance the quality of evaluation and learning processes in higher education institutions.