Analisis Convolutional Neural Network LeNet-5 Dalam Klasifikasi Daun Mangga

Fitri Handayani; Andi Sunyoto; Bayu Anugerah Putra

Fitri Handayani Universitas Muhammdiyah Riau
Andi Sunyoto
Bayu Anugerah Putra

Keywords: Accuracy, Classification, Convolutional Neural Network, LeNet-5, Mango Leaf

Abstract

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.

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References

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Analysis of Convolutional Neural Network LeNet-5 in Mango Leaf Classification

Abstract

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