Comparison of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) Methods in Classifying University Students’ Sleep Quality Based on Academic Assignment Workload

Abstract

A high academic assignment workload often directly reduces the duration and quality of students’ sleep, triggering sleep debt and a decline in both physical and psychological performance. This study aims to analyze the effect of academic assignment volume on students’ sleep duration and to classify their sleep quality using machine learning algorithms. Data were collected through a questionnaire from 150 respondents with 15 variables covering assignment work duration, sleep hours, and assignment pile-up factors. Exploratory Data Analysis (EDA) showed a negative correlation of -0.43 between assignment work hours and sleep duration on active lecture days. Classification modeling was carried out using the Support Vector Machine (SVM) algorithm with an optimized Radial Basis Function (RBF) kernel (C=100, γ=0.01) and K-Nearest Neighbors (KNN) with the best number of neighbors (k=25). The test results show that the SVM model was slightly better overall, with an accuracy of 56.67% and a macro F1-score of 56.62%, compared with the KNN model, which achieved an accuracy of 53.33% and a macro F1-score of 51.39%. In addition, SVM proved better at detecting the poor sleep class, while KNN was more sensitive to the restful sleep class. This study demonstrates that time management and the amount of assignment workload affect students’ rest duration, and that a machine learning approach is able to map these classification patterns despite the challenge of overlapping data.