<![CDATA[This study aims to develop a prediction model for new student admissions at Ulun Nuha High School using the Support Vector Machine (SVM) algorithm. Ulun Nuha High School faces the challenge of fluctuating numbers of applicants every year, which affects resource allocation and strategic planning. The SVM algorithm was chosen because of its ability in classification and regression, so it can identify patterns and trends from historical student admissions data. This study uses data from 100 students with 20 data as the main sample, covering four main variables: Indonesian, Mathematics, Science and Social Studies scores, and memorization. The application of the SVM algorithm in Python obtained prediction accuracy results of 100% from 20 data samples and the results of testing the prediction data resulted in students with registration number 23021 getting a pass result and students with registration number 23022 getting a failure result. The results of the study show that the SVM model can predict the number of new students with high accuracy, close to the real results from historical data. This model provides significant benefits in planning more effective, efficient, and measurable student admissions.]]>
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