Logistic Regression Model for Predicting SNBP Admission Based on Academic Data - Daffa Naufal Rahimi; Yusuf Ramadhan Nasution (APIC, 2026)
The National Selection Based on Achievement (SNBP) is a crucial pathway for prospective students to access higher education; however, the uncertainty surrounding admission outcomes often causes anxiety among prospective students. This study aims to develop an SNBP admission prediction model based on logistic regression using academic data from students at the Darul Arafah Raya Islamic Boarding School. The method used is binary logistic regression with parameter estimation via the Newton-Raphson method. The research data consists of 261 academic records of students from 2022 to 2025, divided into training and testing datasets. Model evaluation was conducted using accuracy, precision, recall, F1 score, and AUC-ROC metrics. The results show that the model achieved convergence at the seventh iteration with an accuracy rate of 81.25 percent. The precision and recall values were 82.35 percent, respectively, while the AUC-ROC value was 0.9049, which falls into the “good classification” category. It can be concluded that the logistic regression model is effective for predicting SNBP graduation based on average report card scores and is suitable for implementation as a decision support system for students in estimating their admission chances.