<![CDATA[Cataract is a condition that causes clouding of the lens of the eye and is a leading cause of blindness, including in Indonesia. Cataract diagnosis is often inconsistent between ophthalmologists due to personal experience. This research proposes a Support Vector Machine (SVM) based classification system and Harmony Search metaheuristic algorithm to optimize the weight vector 'w' on the SVM hyperplane as a supporting tool for cataract diagnosis. The research data comes from Kaggle which includes normal eye fundus images and cataracts with mild-moderate and severe levels. The research stages include image conversion from RGB to Grayscale, image enhancement with Histogram Equalization and GLCE, and feature extraction using GLCM and Haar Wavelet Transform, and unbalanced data is balanced by the SMOTEENN method. The results showed that Harmony Search successfully improved SVM accuracy compared to Conventional SVM using Gradient Descent. Accuracy increased by 18% from 0.53 to 0.71 on unbalanced data, and by 13% from 0.67 to 0.80 on balanced data. In addition, Harmony Search can improve computational time efficiency due to its ability to explore space globally.]]>
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