Academic Performance Prediction Using Supervised Learning Algorithms in University Admission - Gufroni, Acep Irham; Purwanto, Purwanto; Farikhin, Farikhin (JOIV : International Journal on Informatics Visualization, 2025)
Each educational institution has designed an academic system with the aim of providing as perfect learning as possible to students. The quality of good students is influenced by various factors, one of which is the available academic system. Previous research has shown that the quality of a student, which can be called academic achievement, can be determined through historical data on the student admission process. This research aims to process one of the admission processes previously implemented in Indonesian state universities using the National Selection for State University Entrance (SNMPTN) data, combined with Cumulative Achievement Index (GPA) data, so that it can be processed using a machine learning model. The algorithm used to create the model is a Supervised Learning Classification algorithm, which includes a Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB). The research was carried out in three schemes based on the percentages of training data and test data. The results obtained show that DT produces the highest accuracy and precision values, with an accuracy value of 0.79 and a precision value of 0.56, respectively. The XGB produces the highest recall and f1-score values, with a recall value of 0.35 and an f1-score value of 0.36. The model with the highest f1-score can be selected as the best model, namely, the model with the XGB algorithm on a 70%-30% train-test data scheme. The resulting model achieved a success rate of 77%.