International Journal of Academic and Applied Research (IJAAR)   Year: 2023 | Volume: 7 | Issue: 5 | Page No.: 25-30 Application of the Support Vector Machine Classification Method to Elementary School Accreditation Data in Samarinda City Download PDF Marthinus Siahaya, Darnah, Moh. Nurul Huda, Surya Prangga and Meiliani Siringoringo Abstract: Accreditation is recognition of an educational institution after it is assessed that the institution meets standard requirements or predetermined criteria. This accreditation is usually categorized into 4 categories, namely Very good (A), Good (B), Fair (C), and not accredited. The purpose of accreditation is to determine the eligibility level of a school in providing educational services and obtain an overview of school performance. In this study the method used is the Support Vector Machine (SVM) classification, this method is a function model that can describe and differentiate data into classes by finding the best hyperplane to separate two classes of data using a support vector approach. This study uses two internal kernel functions (SVM), namely the linear kernel function and RBF with the proportion of 70%:30% and 90%:10%. The data used is elementary school accreditation data where there are 131 school data using 8 independent variables namely, content standards, process standards, graduate competency standards, educator and education staff standards, facilities and infrastructure standards, management standards, financing standards, and assessment standards . Based on the research that has been done, it is found that the linear kernel function SVM classification method is the most suitable classification algorithm for classifying data in this study with the highest accuracy value of 97.43% with a proportion of 70%:30%.