<![CDATA[This study aims to improve the classification performance of stroke types by optimizing the Support Vector Machine (SVM) algorithm using the Grid Search method. The dataset used consists of medical records of stroke patients in 2024 from RSUD Buleleng, comprising 610 patient records with 13 clinical attributes as input variables and stroke type as the target variable. The research stages include data preprocessing (data cleaning and label encoding), data splitting with an 80:20 ratio, building an SVM model without optimization, and parameter optimization using Grid Search. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the SVM model without optimization achieved an accuracy of 0.92, precision of 0.92, recall of 0.93, and F1-score of 0.92. After applying Grid Search optimization, the best parameters obtained were a polynomial kernel with , , , and , which improved the model performance to an accuracy of 0.95, precision of 0.94, recall of 0.95, and F1-score of 0.94. These results indicate that parameter optimization using Grid Search can effectively enhance the performance of the SVM model in classifying stroke types more accurately.]]>
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