<![CDATA[The selection of a major in Vocational High Schools (SMK) is an important decision that can significantly influence students' future careers. However, in practice, many students choose their majors without adequately considering their academic abilities. This study aims to compare the performance of the Random Forest and Support Vector Machine (SVM) algorithms in predicting the selection of majors among vocational high school students based on academic data. The research employed a data mining approach using the CRISP-DM methodology, which consists of the stages of data understanding, data preparation, modeling, and evaluation. The dataset comprised students' academic scores in Mathematics, Science, Social Studies, Indonesian Language, and English Language. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score as evaluation metrics. The results indicate that both algorithms were able to perform classification effectively. However, the SVM algorithm achieved higher performance with an accuracy of 90%, precision of 91.10%, recall of 89.57%, and F1-score of 89.45%, compared to the Random Forest algorithm, which obtained an accuracy of 86%, precision of 86.62%, recall of 85.88%, and F1-score of 85.27%. Therefore, SVM is considered more effective in predicting vocational high school students' major selection based on academic performance.]]>
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