<![CDATA[Manual identification of chili leaf diseases has the weakness of subjectivity, which impacts the decline in harvest productivity. This study aims to build an accurate automatic classification system using a machine learning approach. The research methodology integrates the extraction of Hue, Saturation, Value (HSV) color features and Gray Level Co-occurrence Matrix (GLCM) texture on a dataset of 1,856 images divided with a ratio of 80:20. Hyperparameter optimization was performed using Grid Search on the K-Nearest Neighbors (K-NN) algorithm to find the best performance. The test results show that the optimal configuration is achieved at a value of K = 3 with the Manhattan distance metric, which produces a test accuracy of 92%. It is concluded that the integration of color and texture features with appropriate parameter optimization is proven to be effective as a reliable and efficient diagnostic solution.]]>
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