<![CDATA[Accurate stroke classification is vital for appropriate clinical treatment. This study compares the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) in classifying ischemic and hemorrhagic stroke using 368 medical records from RSKD Dadi, South Sulawesi. Data were preprocessed using Min-Max normalization and an 80:20 train-test split. ANFIS used Fuzzy C-Means clustering with five fuzzy rules, while ANN used a Multi-Layer Perceptron (10-14-7-2). Evaluated via Confusion Matrix, ANFIS outperformed ANN across all metrics, achieving 82.43% Accuracy, 82.00% Precision, 78.00% Recall, and 79.00% F1-Score, compared to ANN’s 79.73%, 80.00%, 73.00%, and 75.00%. ANFIS is a more effective and interpretable model for supporting stroke diagnosis.]]>
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