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Tomato is one of the farming commodities in Indonesia, easy to plant but easy to get sick. Analizing the disease in plain view still not yet achieve high accuracy result, so we use the help of Convolutional Neural Network (CNN) algorithm. This research is quantitative, with image of a single tomato leaf that is infected as the input. The constructed model gains an accuracy of 58.33% with 12.716 image consisting of Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Target Spot, Spider Mites, Mosaic Virus, Yellow Leaf Curl Virus, Septoria Leaf Spot and healthy leaf. The conclusion from this research is that classification of Tomato leaf disease using CNN can help achieve a higher accuracy but using LeNet-5 as the model architecture is not very effective.
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