Student Academic Performance Prediction Model Based on Machine Learning in PTIK Unimed Tansa Trisna Astono Putri, Reni Rahmadani, Rosma Siregar, Hanapi Hasan, Aida Khairina, Afif Hamzah
Universitas Negeri Medan
Abstract
This research aims to analyze the implementation of machine learning algorithms in predicting the academic performance of students in the PTIK Study Program at Universitas Negeri Medan. The study utilizes several machine learning models, including Decision Tree, Random Forest, and Support Vector Machine, to process academic and demographic data of students. The methodology involves data preprocessing, feature selection, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that machine learning algorithms can effectively predict student academic performance, with the Random Forest model achieving the highest accuracy among the tested algorithms. The findings highlight the potential of machine learning-based prediction models to support early identification of students at risk and inform strategic interventions to improve academic achievement. This research contributes to the development of data-driven decision-making processes in higher education, particularly in the context of the PTIK Study Program at Unimed.
