Klasifikasi Kelayakan Penerima Keringanan Uang Kuliah Tunggal Mahasiswa Politeknik Negeri Bengkalis Menggunakan Algoritma Decision Tree C5.0

The tuition reduction (UKT) assessment process at Politeknik Negeri Bengkalis is still performed manually, resulting in lengthy verification procedures and a high potential for human error. This study aims to develop a classification model for determining student eligibility for UKT reduction using the Decision Tree C5.0 algorithm and to implement it in a web-based application. The dataset includes attributes such as residence, family status, parents’ occupation, parents’ income, number of dependents, ownership of DTKS /KIP /KKS /PKH and ownership of SKTM. The research procedures involved data preprocessing, dataset splitting, model construction, and evaluation using a confusion matrix. The results indicate that the Decision Tree C5.0 model achieved an accuracy of 92%, demonstrating strong and reliable classification performance. The developed web system enhances the decision-making process by reducing subjectivity, improving efficiency, and supporting more accurate eligibility determination for UKT reduction.