Jurnal Teknimedia: Teknologi Informasi dan Multimedia
Vol. 4 No. 2 (2023): Desember 2023

PENGARUH ARSITEKTUR CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT DAUN TOMAT

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

Article Info

Publish Date
06 Dec 2023

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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Journal Info
Jurnal Teknimedia: Teknologi Informasi dan Multimedia
Website

Abbrev

teknimedia

Publisher

STMIK Syaikh Zainuddin Nahdlatul Wathan Anjani

Subject

Computer Science & IT Control & Systems Engineering Engineering

Description

JURNAL TEKNIMEDIA : Teknologi Informasi dan Multimedia terbitan berkala ilmiah nasional diterbitkan oleh STMIK Syaikh Zainuddin NW Anjani. Tujuan diterbitkannya Jurnal TEKNIMEDIA adalah untuk memfasilitasi publikasi ilmiah dari hasil penelitian-penelitian di Indonesia serta ikut mendorong ...