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All Journal Jurnal Riset Informatika Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Pengabdian Kepada Masyarakat Abdi Nusa International Journal Engineering and Applied Technology (IJEAT) Prosiding Seminar Nasional Rekayasa dan Teknologi (TAU SNAR- TEK)
Purnama Insany, Gina
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Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering Public Health Other

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Journal : Jurnal Riset Informatika

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LONG BEAN LEAF DISEASE IDENTIFICATION SYSTEM BASED ON MOBILE USING CONVOLUTIONAL NEURAL NETWORK (CNN) METHOD Muhamad Fadiah Nurjaman; Purnama Insany, Gina; Sanjaya, Imam
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.373

Abstract

Long beans (Vigna unguiculata subsp. sesquipedalis), have high nutritional value, besides long beans also have a significant role in the economy of farmers in Indonesia. However, the productivity of this plant is often hampered by various diseases that attack the leaves, which can result in a decrease in the quantity and quality of the harvest. This study has succeeded in developing a Convolutional Neural Network (CNN) model with the ResNet-50 architecture to identify six types of diseases in long bean leaves. The dataset used consists of 2,316 images, divided into training data (80%), validation (15%), and testing (5%). The ResNet-50 model, which consists of 50 layers, applies the transfer learning technique by not training the first 35 layers using a specific dataset, but utilizing weights from ImageNet. Training for 100 epochs produces high accuracy, namely 98.3% for training data, 98.4% for validation data, and 98.7% for testing data. Evaluation using Confusion Matrix, Precision, Recal and F1 Score shows very good performance without prediction errors. The final result of this research is a mobile-based software system that can diagnose diseases quickly and accurately, which can help farmers take appropriate action, and support sustainable agriculture in Indonesia.
Co-Authors Algifari, M. Alwan Ayu Lestari, Melani Fitri Ramadani, Rizka Hafidzh Al Muhiddin, Muhammad Ibnu Sina, Rivaldi IVANA LUCIA KHARISMA Kamdan Khairiyah Ivanka, Najwa Kharisma, Ivana Lucia Lucia Kharisma, Ivana Mamun, Yuliana Muhamad Fadiah Nurjaman Muhammad Dzulqarnain, Fajar Nasrulloh, Dendi Nuraini, Desti Padilah Qurani, Intan Putri, Hanifa Ramdan Alkausar, Ryan Rizki Firdaus, Asep Rizki Padilah, Gilang Sanjaya, Imam Setiawati Wardin Hia, Srifen Zaki Hibatullah, Muhammad Zalukhu, Eliadi