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All Journal Majalah Kedokteran Bandung Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Jurnal Kreativitas PKM Review of Primary Care Practice and Education (Kajian Praktik dan Pendidikan Layanan Primer) SELL (Scope of English Language Teaching, Linguistics, and Literature) Journal Bulletin of Computer Science and Electrical Engineering (BCSEE) Register : Journal of English Language Teaching and Learning of FBS UNIMED SELL (Scope of English Language Teaching, Linguistics, and Literature) Journal
Amelia, Indah
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Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Bulletin of Computer Science and Electrical Engineering (BCSEE)

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Comparison of Tomato Leaf Disease Detection Using Transfer Learning Architecture with the VGG19 Method Amelia, Indah; Nisar, Nisar
Bulletin of Computer Science and Electrical Engineering Vol. 4 No. 2 (2023): December 2023 - Bulletin of Computer Science and Electrical Engineering
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/bcsee.v4i2.1185

Abstract

Diseases in plants are often detrimental to agriculture, can be seen manually and require a very long time, which can lead to possible errors in disease detection. Detecting diseases in plants early can overcome these problems and reduce the risk of reduced crop production. The aim of this research is to make a comparison of quickly and accurately detecting tomato leaf diseases compared to previous researchers who used Deep Learning applications. Which can be applied effectively for image classification using the VGG19 method. The implementation of this model uses a dataset containing 2,694 images, including 3 different types of diseases. That the conclusion of this research is the fastest and most accurate way to detect tomato leaf diseases. To prove this research, results and necessary data will be presented in this paper. The accuracy obtained on the VGG-19 architecture was 91.85% with the best increase in accuracy compared to the previous journal which only produced 87% accuracy.
Co-Authors Chandra, Paulus Ciptady, Angelica Jovita Fachrina, Fajwa Fikla Fithriyah Rahmawati Insi Farisa Desy Arya Karisma Putri Lestari, Leady Milenia, Tatyana Nabilah, Raihan Shafa Nisar, Nisar Pratama, Liunardo Bintang Rini Handayani Safnul, Farhan Bariq Say, Yosefina Dwikurnia Gufra Putri Sharon Gondodiputro Simandjuntak, Matthew Prima Haratua Subagdja, Muhammad Fauzan Meidika Syifa, Diandra Salsabila Utami, Nita Aprilia Zulfitri Zulfitri, Zulfitri