<![CDATA[Predicting students' on-time graduation is an important indicator in evaluating the quality of higher education institutions, as it is closely related to learning effectiveness and academic success. This study aims to develop a student graduation prediction model using the Support Vector Machine (SVM) algorithm based on academic data. The dataset consists of 50 student records with attributes including Grade Point Average (GPA), completed credit units, failed courses, semester, and graduation status. The research applies several preprocessing stages, including the removal of irrelevant attributes, label encoding, data normalization using StandardScaler, and dataset splitting into training and testing sets with an 80:20 ratio. The SVM model is built using the Radial Basis Function (RBF) kernel to classify student graduation status. Model performance is evaluated using a confusion matrix, accuracy, precision, recall, and F1-score metrics. The experimental results show that the SVM model achieves an accuracy of 90%, precision of 88%, recall of 92%, and an F1-score of 90%. These findings indicate that the SVM algorithm is effective in identifying academic patterns and accurately classifying student graduation status. This study is expected to serve as a foundation for developing decision support systems and early warning systems to assist higher education institutions in identifying students who are at risk of delayed graduation.]]>
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