<![CDATA[Eye diseases such as cataracts, diabetic retinopathy and glaucoma are the leading causes of visual impairment and blindness worldwide, so early detection through medical image analysis is essential to prevent complications and permanent vision loss. The development of artificial intelligence and machine learning technology provides great opportunities to help medical personnel carry out diagnoses more quickly, accurately and efficiently. This research aims to develop an eye disease classification model using the Random Forest algorithm with hyperparameter optimization to differentiate four eye conditions, namely cataract, diabetic retinopathy, glaucoma, and normal. The dataset used is sourced from the public and consists of eye fundus images that have gone through preprocessing and feature extraction to improve data quality. The data was divided into training and testing, then the Random Forest model was trained with hyperparameter optimization using RandomizedSearchCV for 20 iterations and 5-fold cross-validation to obtain the best parameter combination. The best model achieved an accuracy of 80.92% on testing data with a macro ROC-AUC value of 0.9422, where the best performance was obtained in the classification of diabetic retinopathy with a precision of 99.54%, recall of 99.09%, and ROC-AUC of 1.0000. In addition, macro specificity reached 93.66%, indicating the model's good ability to identify negative cases correctly. The research results show that the Random Forest approach with hyperparameter optimization has excellent performance for eye disease classification and has the potential to be implemented as an artificial intelligence-based medical diagnosis support system in health care facilities.]]>
