Pemodelan Klasifikasi Penyakit Daun Tanaman Tomat dengan Convolutional Neural Network Algorithm - Goklas Henry Agus Panjaitan; Simatupang, Frengki (STMIK Budi Darma, 2024)
Tomatoes constitute a horticultural crop with significant economic value and are extensively cultivated by the people of Indonesia, an agricultural country. Tomato productivity is anticipated to increase in 2021-2022. However, issues often arise during the plant care process when attempting to identify the type of disease affecting the leaves of tomato plants, owing to the nearly identical symptoms that make differentiation challenging. Tomato plant leaves play a crucial role in the plant growth process. Addressing the challenge of tomato leaf diseases necessitates a method that employs specific technology for easy detection. Before implementing this method, a classification model for tomato leaf plants must be developed. The goal of this modeling is to create an effective tomato leaf classification model for subsequent use in a disease detection system that utilizes IoT technology. The modeling of tomato plant disease classification involves the application of the Convolutional Neural Network algorithm. The modeling process encompasses data source analysis, data preprocessing, model formation, model training, model evaluation, and the subsequent acquisition of model results. The dataset utilized for implementation comprises 18,162 images of tomatoes. The accuracy of the training data surpasses that of the validation data, indicating the reliability of the data used for model development. The accuracy of the training data is recorded at 94.06%, signifying its suitability for modeling. Additionally, the loss function result on the training data is 7.8%, further affirming the quality of the training data for model development.