Article Per Year (5 Year)
p-Index From 2021 - 2026
1.687
P-Index
This Author published in this journals
All Journal International Journal of Informatics and Computation Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Teknologi Kharisma Tech Computing and Education Technology Journal Kenduri: Jurnal Pengabdian Dan Pemberdayaan Masyarakat
Better Swengky
Politeknik Manufaktur Negeri Bangka Belitung
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Social Sciences Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
RiceGuard: A Lightweight PSO-Optimized MobileNetV2 Framework for Stable Rice Leaf Disease Classification M. Hizbul Wathan; Better Swengky; Indra Irawan; Fatir Atthariq Zami
Computing and Education Technology Journal Vol 6, No 1 (2026): APRIL
Publisher : Pendidikan Komputer FKIP Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/cetj.v6i1.18663

Abstract

Rice leaf diseases such as blast, brown spot, bacterial blight, and tungro pose a serious threat to agricultural productivity, with potential yield losses exceeding 50% under epidemic conditions. Therefore, rapid and accurate early detection is essential to support sustainable food security and precision agriculture. This study proposes a rice leaf disease classification system based on the MobileNetV2 architecture optimized using Particle Swarm Optimization (PSO). A dataset consisting of four rice leaf disease classes, collected from public repositories and sources representing real field conditions, is used to evaluate the proposed approach. Transfer learning is employed to improve training efficiency, while PSO is applied to optimize key hyperparameters to enhance model stability and convergence.Experimental results show that MobileNetV2 optimized with PSO consistently achieves superior classification performance and improved training stability compared to the standard MobileNetV2 baseline. The baseline MobileNetV2 achieves 92% accuracy, with the highest F1-score of 0.99 on the tungro class and the lowest performance of 0.88 on the blast class. In contrast, MobileNetV2–PSO demonstrates a significant improvement, reaching 99% accuracy, with F1-scores of 0.98 for bacterial blight, 0.98 for blast, 0.99 for brown spot, and 1.00 for tungro. The largest improvement occurs in the blast class, with a 10-point increase in F1-score, indicating that PSO optimization provides greater sensitivity to complex disease patterns that were previously difficult to classify.These findings indicate that the proposed framework provides an accurate and lightweight solution with strong potential for deployment on mobile and resource-constrained platforms to support intelligent rice disease diagnosis.
Co-Authors Ahmat Josi Amanda Saputri Aulia, Rosaura Bradika Almandin Wisesa Cahyadi, Irsan Christianty, Licia Dora Palupi Evvin Faristasari Fatir Atthariq Zami Firmansyah, Muhammad Ferdi Firmansyah Indra Irawan Indra Irawan Irwan Irwan Julianto, Fadil LINDA FUJIYANTI M. Hizbul Wathan Mahmudin Mahmudin Muhamad Galy Njoman Ari Pribowo Ramadani, Ardi Robert Napitupulu Sidhiq Andriyanto Silvia Agustin Sirlus Andreanto Jasman Duli Susianti, Helda Vivin Mahat Putri Yus Dwi Yanti Zain, M Syafrizal