Comparison of Tomato Leaf Disease Classification Accuracy Using Support Vector Machine and K-Nearest Neighbor Methods

Tomato Leaf Disease is one of the common things for farmers in growing tomatoes. Tomatoes are popular crops that can grow low and high areas but susceptible to disease. For this reason, take precautions by looking at characteristics texture tomato leaves. However, requires more time money a long process. One efforts be made classify leaf diseases. This research aims using Support Vector Machine K-Nearest Neighbor methods. The dataset used image data with 4 classes leaves affected disease 1 healthy leaf. We evaluate analyze all models 5-Fold, 10-Fold, 20-Fold Cross Validation accuracy, precision, recall best accuracy. results study accuracy SVM method 0.953 or 95.3%, Precision Recall 95.3% 10-Fold Cross-Validation. Compared K-NN method, it only obtained an 0.907 90.7%, 0.908 90.8%, 90.7%