Analysis of Strategies for Improving Learning Quality Based on Naive Bayes and Support Vector Machines

Abstract

Islamic educational institutions, particularly Islamic boarding schools, face increasing challenges in improving the quality of learning. The learning quality in Islamic boarding schools should be analyzed in depth to support effective improvement strategies. Based on this background, this study aims to classify strategies for enhancing learning quality using the Naïve Bayes and Support Vector Machine (SVM) algorithms. Naïve Bayes with a Gaussian distribution is widely recognized for its simplicity and accuracy in data classification. Meanwhile, Support Vector Machines (SVM) with a linear kernel are effective for linearly separable and high-dimensional data, enabling stable and efficient modeling in the context of data-driven analysis of learning quality in formal education. The data were collected through questionnaires distributed to 100 female students and 100 teachers. The variables examined include teacher competence, infrastructure, school management, student participation, and learning quality level. The analysis results indicate that the Naïve Bayes algorithm achieved superior performance with an accuracy of 90%, precision of 95.65%, recall of 83.33%, and an F1-score of 86.56%. In contrast, the Support Vector Machine (SVM) obtained an accuracy of 80%, precision of 58.97%, recall of 66.67%, and an F1-score of 62.32%.These findings demonstrate that Naïve Bayes provides more stable classification performance across all learning quality categories. Conversely, the Support Vector Machine (SVM) shows less optimal performance in the low-quality class due to the limited number of data samples. This study contributes effectively to the classification of learning quality levels in Islamic boarding schools