Analisis Convolutional Neural Network LeNet-5 Dalam Klasifikasi Daun Mangga - Fitri Handayani; Andi Sunyoto; Bayu Anugerah Putra (Universitas Muhammadiyah Riau, 2024)
Mango is one of the agricultural productions. Like other agricultural crops, diseased mango leaves are a production problem. As a result, agricultural productivity decreases. This research aims to classify healthy or diseased mango leaves by developing a Convolutional Neural Network (CNN) based system with LeNet-5 feature extraction. The dataset used is sourced from Mendeley consisting of healthy leaf types totaling 265 images and diseased totaling 170 images. The data division used consists of 80% training data and 20% test data. The augmentation process is carried out to reduce over fitting. The results showed that the epoch process stopped at the 20th epoch and resulted in 93% accuracy. This shows that the CNN method for image classification can produce accurate accuracy in solving real-world problems.