<![CDATA[Eye diseases are one of the health disorders that can have serious consequences if not diagnosed early. In an effort to improve the accuracy and efficiency of eye disease detection, the Support Vector Machine (SVM) method is used for classifying eye diseases based on image datasets or related numerical data. This research aims to implement SVM as a classification algorithm, utilizing features extracted from eye images or relevant medical data. The research process includes data collection, preprocessing, feature extraction, SVM model training, and model performance evaluation using accuracy, precision, recall, and F1-score metrics. By applying k-fold cross-validation techniques, the model is tested to avoid overfitting and ensure good generalization. The results of the study show that the SVM method can provide accurate classification results and can be used as an effective tool for diagnosing eye diseases.]]>
Copyrights © 2024
