Comparison of K-Nearest Neighbor, Naïve Bayes, and C4.5 Algorithms for Predicting Academic Stress Risk in Students Based on Psychological Survey Data - Widya Pertiwi, Nur Annisa; Wibisono, Iwan Setiawan (Politeknik Negeri Batam, 2026)
Academic stress is a psychological problem experienced by many students and can have an impact on learning achievement, mental health, and quality of life. This study aims to compare the performance of the K-Nearest Neighbor (KNN), Naïve Bayes, and C4.5 (Decision Tree) algorithms in predicting the level of academic stress risk in students based on psychological survey data. Data were obtained from 700 active students at Ngudi Waluyo University through a questionnaire covering physiological, psychological, and behavioral aspects, with a total of 15 indicators using a Likert scale. The data then underwent pre-processing, labeling, standardization, and division into training and test data with a ratio of 80:20. The evaluation was conducted using the Accuracy, Precision, Recall, F1-Score, Confusion Matrix, and AUC-ROC metrics. The results showed that the Naïve Bayes algorithm performed best with an accuracy of 93.26%, precision of 93.35%, recall of 92.26%, and F1-score of 92.80%. The KNN algorithm was in second place with an accuracy of 91.43%, while the C4.5 algorithm had the lowest performance with an accuracy of 80.60%. Based on these results, Naïve Bayes is recommended as the most optimal algorithm for predicting academic stress risk in students using psychological survey data. This study is expected to assist educational institutions in identifying students at risk of stress early on and supporting the development of more effective prevention strategies.