<![CDATA[This study aims to analyze the impact of Whale Optimization Algorithm (WOA) optimization on the performance of Support Vector Machine (SVM) in classifying images of coffee leaves treated with NPK fertilizer. WOA is employed to find the optimal combination of SVM parameters to improve classification accuracy. The dataset consists of coffee leaf images that have undergone feature extraction based on color and texture. Performance evaluation was conducted using a confusion matrix, classification report, and heatmap visualization. The results show that the SVM model optimized with WOA performs better than the non-optimized SVM. Specifically, the non-optimized SVM achieved a precision of 0.82, recall of 0.81, and F1-score of 0.81. After optimization with WOA, the model’s precision increased to 0.90, recall to 0.88, and F1-score to 0.87. This study demonstrates that metaheuristic approaches like WOA can significantly enhance the performance of classification algorithms in the context of digital image processing. The findings have practical implications for early detection of plant quality through image-based analysis in technology-driven agriculture]]>
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