KLASIFIKASI PENYAKIT DAUN TOMAT DENGAN METODE CONVOLUTIONAL NEURAL NETWORK MENGGUNAKAN ARSITEKTUR INCEPTION-V3 - Nurdin, Andi; Yudha Kartika, Dhian Satria; Efrat Najaf , Abdul Rezha (LPPM Universitas Putera Batam, 2024)
Tomato is an important food crop in the world, including Indonesia, which often faces leaf diseases such as mosaic virus, target spot, bacterial spot, yellow leaf curl virus, late blight, leaf mold, early blight, spider mites, and septoria leaf spot. These diseases are difficult to recognize manually, so Deep Learning technology, especially Convolutional Neural Network (CNN), is used for the classification process. This research uses Inception V3 transfer learning model and image preprocessing and data augmentation. Testing is done with various optimizers (Adam, SGD, RMS Prop) to find the best model. Tests were conducted using confusion matrix to determine the level of accuracy produced by the CNN model. The results show an accuracy of 93.8% with the optimal model using Optimizer Adam.