<![CDATA[Indonesia’s coffee industry plays a crucial role as a major export, making a significant contribution to the country’s economy by generating foreign exchange. The quality and quantity of coffee production depend on various factors such as humidity, rain, and fungus that can cause rust diseases on coffee leaves. These diseases can spread quickly and affect other coffee plants quality, leading to decreased production. To address this issue, CNN with VGG-19 architecture model was utilized to identify coffee plant diseases using image data and the python programming language, which in previous studies used MATLAB as their platform. In addition, VGG-19 with image enhancement and contouring data for pre-processing step has a more profound learning feature than the method used in the previous studies, AlexNet which makes the structure of VGG- 19 more detailed. The dataset used in this paper is Robusta Coffee Leaf Images Dataset which have three classes, namely health, red spider mite, and rust. The VGG-19 model attained F1-Score of 90% when evaluated using the testing data with ratio 80:20, where 80% is training data, and 20% is validation data. This paper employed 0.0001 learning rate, batch size 15, momentum 0.9, 12 training iteration, and RMSprop optimizer.]]>
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