Implementasi Metode C.45 dalam Prediksi Status Pembayaran Mahasiswa Baru ITPLN

Abstract

Predicting student payment behavior is important for higher education institutions as timely payments are crucial for their financial stability and sustainability. Factors such as demographics, financial, and behavioral characteristics can influence payment outcomes, but previous research has focused on traditional on-campus students and ignored other student populations. This study aims to fill this gap by predicting student payment behavior at a specific institution using a dataset that includes demographic information, financial data, and behavioral indicators. The C4.5 decision tree algorithm was applied to this data to construct a predictive model, which was then evaluated using performance metrics. The findings of this study will help the institution design targeted interventions to improve payment rates and support its financial stability.