Prediksi Kelulusan Tepat Waktu Program Pendidikan Profesi Dokter Gigi Menggunakan Algoritma Random Forest

Abstract

The number of dentists in Indonesia as of May 2025 remains relatively low compared to the population size. The government expects higher education institutions as producers of dental graduates to increase the number of graduates in order to meet the national healthcare workforce demand. This condition has become an important concern for Dental Professional Education Programs, particularly the Faculty of Dentistry at Sultan Agung Islamic University, not only in supporting accreditation achievement but also in contributing to the increase in the number of dentists in Indonesia. Various efforts have been implemented, such as clinical supervision, evaluation of clinical learning outcomes, and comprehensive examinations. However, in the implementation of the Dental Profession Student Competency Examination (UKMP2DG), there are still students who fail to pass the examination. This study offers an innovative solution by utilizing Machine Learning technology to predict student graduation based on academic data using the Random Forest algorithm. The research data include UKMP2DG participants from period 1 of 2020 to period 1 of 2025. The results show that the Random Forest model provides good predictive performance with an accuracy of 83.19%, precision of 88.51%, recall of 89.53%, and F1-score of 89.02%. In addition, the most influential feature in predicting graduation was the undergraduate Grade Point Average (GPA), with a contribution level of 41.57%. The findings of this study are expected to serve as a basis for academic decision-making and assist institutions in improving student development strategies to enhance the UKMP2DG graduation rate.