<![CDATA[This study aims to develop an efficient system for detecting retinal diseases from fundus eye images by applying Deep Learning with Convolutional Neural Networks (CNN). The proposed model is designed to assist ophthalmologists in making accurate and timely diagnoses, enabling appropriate treatment and improving patient outcomes. The research emphasizes the role of CNN-based Deep Learning as a reliable method for classifying retinal disorders. A quantitative approach was employed, utilizing numerical and descriptive data such as images, observations, and secondary sources. The research procedure covered several stages: image preprocessing, CNN model design, training, validation, evaluation, and system testing. The experimental results demonstrated that the developed system achieved an accuracy of 97%. Evaluation metrics confirmed high performance with classification results as follows: Myopia (precision 1.00, recall 1.00, f1-score 1.00), Cataract (precision 0.88, recall 1.00, f1-score 0.93), Diabetic Retinopathy (precision 1.00, recall 1.00, f1-score 1.00), and Glaucoma (precision 1.00, recall 0.95, f1-score 0.97). These findings show that the CNN architecture with VGG16 demonstrates excellent capability in detecting and classifying retinal diseases using fundus images. Therefore, the model can be recommended as a practical tool for early detection of retinal disorders, particularly within the context of healthcare services in Kendari City, Southeast Sulawesi.]]>
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