| Summary: | Tuition fee is the payment of tuition fees each semester borne by each student based on their economic capabilities. Tuition fee is divided into five groups from tuition fee group 1 to tuition fee group 5. This research aims to find the accuracy of the classification of tuition fee using Support Vector Machine (SVM). SVM is a method used for classification of the concept to find hyperplane (separator function) that can separate the data into a predetermined class. In this research, SVM is used to determine the accuracy of tuition fee classification. The variables used are income parents, father’s occupation, mother’s occupation, home ownership status, building, land area, electricity cost, water cost, phone cost, saving accounts, jewelry ownership, and a premium ownership. The results obtained are five hyperplanes to separate tuition fee with accuracy of the classification of tuition fee was 59,69%.
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