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All Journal Asian Journal of Social and Humanities
Nugraha, William
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Decision Sciences, Operations Research & Management Earth & Planetary Sciences Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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Comparison of SVM, KNN, and Naïve Bayes Classification Methods in Predicting Student Transfers at BK Palu School Nugraha, William; Firmansyah, Gerry; Mulyo Widodo, Agung; Tjahjono, Budi
Asian Journal of Social and Humanities Vol. 3 No. 1 (2024): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v3i1.413

Abstract

Student transfers are a significant issue in schools and can affect the dynamics of education and student performance. This research aims to predict student transfers using a comparative analysis of three classification methods: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes. The study utilizes historical data from BK Palu School, covering the years 2022 to 2024, which includes demographic, academic, socio-economic, and student quality information. The methodology involves data collection, data preparation, algorithm selection, implementation, and evaluation of the three methods. The performance of the classification methods is assessed using metrics such as accuracy, precision, recall, and F1-score. The results indicate that SVM has the highest accuracy in predicting student transfers, followed by KNN and Naïve Bayes. This study contributes to identifying key factors influencing student transfers and offers schools a robust model to develop targeted strategies for reducing transfer rates. Ultimately, this research provides insights into optimizing student retention and improving the overall quality of education.
Co-Authors BUDI TJAHJONO Firmansyah, Gerry Mulyo Widodo, Agung