PENGARUH ARSITEKTUR CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT DAUN TOMAT

Rizky Arya Kurniawan; Andi Sunyoto; Asro Nasiri

doi:10.46764/teknimedia.v4i2.111

Rizky Arya Kurniawan Universitas Amikom Yogyakarta
Andi Sunyoto Magister Teknik Informatika, Universitas Amikom Yogyakarta
Asro Nasiri Magister Teknik Informatika, Universitas Amikom Yogyakarta

DOI: https://doi.org/10.46764/teknimedia.v4i2.111

Keywords: Convolutional Neural Network, Classification, Tomato Leaf Disease

Abstract

Plant disease is one of the crucial factors in plant survival. Tomato plants also need early help to be able to deal with disease problems. One of the organs of the tomato plant that is commonly attacked by diseases is the leaves. By providing assistance early on, it can prevent crop failure. Of course, having a trained system can reduce the cost of a farmer in dealing with diseases without expert assistance. In this research, we will test the ability of the CNN architecture to classify tomato leaf disease images. The dataset used is 4079 image data which are divided into 3 disease classes. From the results of experiments that have been carried out the InceptionV3 architecture gets the best results with an accuracy rate of 100%, ResNet50 has 97,36% accuracy and MobileNet 85,81%.

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