PENGARUH CONVOLUTIONAL NEURAL NETWORK UNTUK PROSES DETEKSI PENYAKIT PADA DAUN TOMAT

Tomato plants are one of the plants that are often planted by farmers and are the main food requirement in society. Tomato cultivation is often faced with disease problems that can attack the leaves, stems and fruit. However, many farmers often face difficulties in overcoming this problem. To solve this problem, researchers will use a web-based system that is able to classify images of tomato leaves. The system will process the image first before training the CNN model. The resulting model will be used to classify images entered through the website. Apart from that, this design also has several useful benefits. The results of the analysis of the model show that there are challenges in distinguishing the characteristics of diseases in tomato plants, so that the development of the CNN model experiences difficulties. Despite these difficulties, the CNN algorithm provides an accuracy score of 0.9091. This number reflects the model's level of accuracy in classifying images into the correct categories. From these results, it can be concluded that disease detection in tomato plants using the CNN algorithm requires special effort and attention, especially in collecting representative datasets and modeling optimal CNN architecture. A deeper understanding of the characteristics of diseases in tomato plants also needs to be considered to increase the accuracy of model predictions. Although there is still room for improvement, these results provide a basis for continuing to develop and improve disease detection models in tomato plants using CNN approaches.