Forecasting graduate student enrollment in university using regression analysis

Abstract

The government ensures educational quality in universities through a quality assurance (QA) system implemented via accreditation, which evaluates both study programs and institutions. A key concern in accreditation is the decline in new student enrollment, making accurate predictions of enrollment numbers essential for quality assessment. This study proposes a linear regression (LR) model to forecast future university student enrollments based on enrollment figures from the previous year as input feature. Using a dataset from one of Indonesia’s leading university spanning 2013 to 2023, the experimental results demonstrate that the LR model outperforms other regression techniques, including multi-layer perceptron (MLP), K-nearest neighbors (KNN), decision tree (DT), and random forest (RF). The LR model achieves R² values between 0.87 and 0.95, reflecting a strong linear relationship between current and future student numbers. It also delivers high accuracy, with root mean square error (RMSE) values ranging from 11.72 to 41.21 per year. The trained LR model has been integrated into a web-based system, offering data visualization and enrollment predictions to support university management in monitoring quality, addressing enrollment challenges, and facilitating informed decision-making.