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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Multica Science and Technology
Ilham Sahputra
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Computer Science & IT Energy Engineering Library & Information Science Physics

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

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Comparative Study of VGG16 and MobileNet Architectures for Rice Leaf Disease Classification Using CNN Ilham Sahputra; Ananda Faridatul Ulfa; Bella Amanda Putri; Cut Yuniza Eviyanti
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.15458

Abstract

Rice is a primary commodity in Indonesia's agricultural sector, playing a vital role in national food security. However, rice productivity is frequently disrupted by leaf diseases such as Bacterial Leaf Blight, Brown Spot, Leaf Blast, and Narrow Brown Spot. This study aims to develop an automated rice leaf disease identification model using the Convolutional Neural Network (CNN) method with a transfer learning approach. Two CNN architectures, VGG16 and MobileNet, were trained using a dataset of 2,190 rice leaf images divided into five classes. The research process included data collection, preprocessing, model training, and performance evaluation using a confusion matrix. The results show that the VGG16 model achieved an accuracy of 98%, while MobileNet reached 95% accuracy. Thus, this method can serve as a modern solution for identifying rice plant diseases, supporting early detection efforts and enhancing agricultural productivity.
Co-Authors Ananda Faridatul Ulfa ANNISA KARIMA Bella Amanda Putri Difa Angelina Eviyanti, Cut Yuniza Irvan Na’syakban Mutammimul Ula Natasya Natasya Rizky Putra Phonna Siti Fatimatun Zahro Sujacka Retno T. Sukma Achriadi Sukiman Veri Ilhadi