<![CDATA[Stroke is one of the most dangerous non-communicable diseases and a leading cause of death and long-term disability worldwide, including in Indonesia. Early detection of stroke risk is essential to prevent more severe conditions and to accelerate medical treatment. This study aims to develop an early detection system for stroke using the Support Vector Machine (SVM) algorithm. The dataset was obtained from Kaggle (Stroke Prediction Dataset), consisting of 5,110 patient records with features such as age, gender, hypertension, heart disease, work status, body mass index (BMI), average glucose level, and smoking status. The data underwent preprocessing, including data cleaning, categorical encoding, normalization using a Standard Scaler, and an 80:20 train-test split. The SVM model was tested using four kernel types (linear, polynomial, RBF, and sigmoid) to obtain the most optimal configuration. The RBF kernel achieved the best performance, with 91% accuracy during kernel comparison and a final accuracy of 84.58%, average precision of 0.85, average recall of 0.85, average F1-score of 0.85, and an AUC of 0.92. The system was implemented as a Streamlit-based dashboard so that it can be used as an early screening tool for stroke risk by medical personnel and the general public.]]>
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