Analisis Rasio Siswa dan Guru dalam Penentuan Kinerja Sekolah di Berbagai Wilayah Indonesia Melalui Metode Naive bayes

Abstract

School performance assessment is a crucial aspect of improving the quality of education in Indonesia. This study aims to classify school performance using the Naive Bayes method, based on the student-to-teacher ratio and other supporting variables. The dataset used includes 400 schools in various locations with attributes such as school name, location, education level, number of students, number of teachers, and student-to-teacher ratio. School performance labels are categorized into three categories: Good, Adequate, and Poor. The Naive Bayes method is applied to predict performance categories based on the relationship patterns between variables in the dataset. Experimental results show that the Naive Bayes method achieves a classification accuracy of [insert accuracy value if available], with the student-to-teacher ratio being the most significant factor in predicting performance. This study demonstrates the potential of using probabilistic-based methods to support decision-making in school evaluation and management, and provides recommendations for education policy to maintain an ideal student-to-teacher ratio.