<![CDATA[Sugarcane is one of the important agricultural crops in Indonesia, playing a strategic role in maintaining national food security as well as serving as a raw material for the sugar industry. However, plant diseases such as mosaic, rust, red rot, and yellow can significantly reduce sugarcane productivity. This study aims to develop a web-based system capable of classifying diseases in sugarcane leaf images using EfficientNet-B7 as a feature extractor and Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel as the classifier. The research method includes preprocessing steps such as resizing, cropping, and image augmentation, followed by feature extraction and classification using Stratified K-Fold Cross Validation. Research results indicate that the model achieved optimal performance with a C value of 10 and a Gamma value of 0.001 resulting in an accuracy of 93.33%. Furthermore, alpha testing showed that the application operated without errors, while beta testing with 35 respondents resulted in an average user satisfaction rate of 91.82%. These results demonstrate that the developed system functions effectively, is accurate, and easy to use. This application has the potential to serve as a tool for early detection of sugarcane leaf diseases, thereby supporting improved agricultural management and productivity.]]>
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