<![CDATA[This study aims to develop a classification system for determining the quality of curly lettuce leaves into two categories: suitable for consumption and not suitable for consumption, using the Random Forest method. The advancement of digital image processing technology enables the identification of plant quality to be carried out automatically and more efficiently compared to manual methods. The dataset used consists of lettuce leaf images processed through several stages, including image preprocessing, feature extraction, and classification. In the feature extraction stage, color features in the HSV color space and texture features using the Gray Level Co-occurrence Matrix (GLCM) method are applied. These features are then used as input for the classification process using the Random Forest algorithm. The test results show that the developed model achieves good performance with an accuracy of 91%, precision of 91.8%, recall of 90%, and F1-score of 90.9%. This indicates that the system is capable of classifying lettuce leaf quality with a high level of accuracy. Therefore, the developed system can assist in automatically identifying the quality of lettuce leaves and can serve as an alternative solution to support technology-based agriculture.]]>
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