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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JPM (Jurnal Pemberdayaan Masyarakat) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL PENGABDI JMM (Jurnal Masyarakat Mandiri) JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Teknologi Informasi dan Multimedia Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Soft Computing Exploration Jurnal Pengabdian Masyarakat Indonesia Media Jurnal Informatika Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Interaktif: Warta Pengabdian Pendidikan (JIWPP) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Paradigma INCOME: Indonesian Journal of Community Service and Engagement Insand Comtech : Information Science and Computer Technology Journal Journal of Artificial Intelligence and Digital Business Bakti Sekawan : Jurnal Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering Prosiding Seminar Nasional CORISINDO
Hammad, Rifqi
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Journal : Media Jurnal Informatika

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Rice Leaf Disease Classification Based on ResNet50 and MobileNetV3 Feature Extraction Using Random Forest Pratama, Gede Yogi; Husaini, Rahayun Amrullah; Nasri, Muhammad Haris; Hammad, Rifqi
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5939

Abstract

Diseases in rice plants are one of the main factors contributing to decreased agricultural productivity. Early and accurate disease identification is crucial to support effective decision-making in plant disease management. This study aims to compare the performance of deep learning models based on Convolutional Neural Networks (CNN), namely ResNet50 and MobileNetV3, as well as their integration with the Random Forest (RF) algorithm for rice leaf disease classification. The dataset used consists of rice leaf images categorized into several disease classes. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics with a macro-average approach. The results show that the standalone ResNet50 and MobileNetV3 models achieved accuracies of 62.5% and 65.7%, respectively, with macro F1-scores below 0.65, indicating moderate classification performance. However, combining CNN models with Random Forest significantly improved classification performance. The ResNet50 + RF model achieved an accuracy of 99.6%, while the MobileNetV3 + RF model attained the highest accuracy of 99.8%, along with equally high macro-averaged precision, recall, and F1-score values. These findings demonstrate that integrating CNN-extracted features with the Random Forest algorithm enhances the model’s ability to distinguish disease classes more accurately and consistently. Therefore, the hybrid CNN–Random Forest approach shows strong potential as an effective solution for image-based rice plant disease detection systems.
Co-Authors Abdul Muhid, Abdul Abdurahman Abdurrahman Ahmad Ahmad Ahmat Adil Al-Mu’min, Al-Mu’min Amrullah, Ahmad Zuli Andi Sofyan Anas Apriani Apriani Apriani Arfa, Muhammad Astuti, Emi Attaqwa, M.Aswin Syarif Azhar, Raisul Azhari, Anjas Ardiyan Azkari, Adzan Naufal Bukran Cahyablindar, Ayu Cokorda Javandira Fatimatuzahra, Fatimatuzahra Fatimatuzzahra Fatimatuzzahra Fatimatuzzahra Guyup Mahardhian Dwi Putra Habib Ratu Perwira Negara Hairani Hairani Hardita, Veny Cahya Harisandi, Lalu Ilham Hidayat, Fadila Ananda Kartika Husain Husain Husaini, Rahayun Amrullah Husnita Komalasari I Made Yadi Dharma I Nyoman Switrayana Ida Ayu Made Dwi Susanti Kartarina, Kartarina Kurniadin Abd Latif Kusmayadi, Iwan Lestari, I Desak Ayu Adhia M. Hidayatullah Mardedi, Lalu Zazuli Azhar Melati Rosanensi Miftahul Madani Muhammad Azwar Muhammad Haris Nasri Muhammad Innuddin Muhammad Mujahid Dakwah Neny Sulistianingsih Nyoman Yudiarini Pahrul Irfan Panca Mukti, M Thoric Pratama, Gede Yogi Puspita Dewi, Puspita Putri, Destiana Adinda Qososyi, Sayidina Ahmadal Qulub, Mudawil rodhi, mohammad najib Roodhi, Mohammad Najid Samudra, Nanang Santoso, Heroe Saputra, Ahmad Hakiki Sembiring, Rinawati Sholeha, Eka Wahyu Sirojul Hadi Sujaka, Tomi Tri Sujaka, Tomy Tri Sukmawaty Sukmawaty, Sukmawaty Suprayetno, Djoko Suriyati ., Suriyati Syahrir, Moch. Syarif, M. Aswin Tajuddin, Muhammad Tanwir Tanwir Tri Sujaka, Tomi Wirajaya Kusuma Yuliana Yuliana Zamroni Alpian Muhtarom Zuhrian, Naufal Rifqi Zulfikri, Muhammad