Seleksi Fitur SelectKBest Dalam Prediksi Kelulusan Mahasiswa Tepat Waktu dengan Decision Tree - Putra, Yogi Erka Julyansa; Imelda, Imelda; Suryadih, Suryadih (Building of Informatics, Technology and Science (BITS), 2025)
University in Jakarta are facing issues with a surge in the number of students not graduating on time within the organizational office. This recommendation analyzes the performance used to predict timely student graduation. The primary objective of this consideration is to create an estimation illustration using a Decision Tree. The data used combines information about various components. The evaluation of the emerging execution is based on metrics such as accuracy, precision, and review. Timely graduation provides various benefits to colleges. Firstly, it enhances the institution's reputation as a provider of quality instruction that supports students in completing their studies almost on time. Colleges can improve the quality and sustainability of instruction by implementing methods based on this demand. The consideration involves creating a desktop application to input substitute student data and predict whether each substitute student will graduate on time or not. This examination makes a significant contribution to efforts aimed at advancing the quality of teaching in colleges and helping substitute students better achieve their academic goals. The data, collected from universities in Jakarta, consists of 308 student records. The results almost illustrate that the model using highlight assurance yields an accuracy of 97.85%, while the model without highlight options yields an accuracy of 93.54%.