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All Journal Jupiter Syntax Jurnal Informatika Jurnal Algoritma Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Aisyah Journal of Informatics and Electrical Engineering Journal of Information Systems and Informatics Abdimas Universal Yumary: Jurnal Pengabdian kepada Masyarakat Jurnal Abdimas Bina Bangsa Jurnal Algoritma Jurnal Sistem Informasi dan Teknologi (SINTEK) IJOEM: Indonesian Journal of Elearning and Multimedia Carmin: Journal of Community Service LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Jurnal Rekayasa Perangkat Lunak
Ratnasari Ratnasari
Departemen Rekayasa Perangkat Lunak, Fakultas Teknologi Dan Informatika Universitas Aisyah Pringsewu, Lampung - Indonesia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Deteksi Penyakit Daun Padi Menggunakan Deep Learning untuk mendukung Produktivitas dan Pertanian Berkelanjutan Ratnasari; Feriyanto, Dwi
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2900

Abstract

Rice is a major food commodity that is susceptible to leaf diseases, such as blast, bacterial leaf blight, and tungro, which can significantly reduce productivity if not detected early. This study aims to develop an early detection method for rice leaf diseases using a deep learning approach with a VGG16-based Convolutional Neural Network (CNN) architecture. The data used came from the Rice Leaf Dataset (Kaggle) and field images in Pringsewu Regency. The training process was carried out through transfer learning. The results showed that the model was able to achieve an accuracy of 99.75% on the training data, 96.08% on the validation data, and 100% on the test data. Field tests also proved the model's ability to generalize to real conditions, although there were still some cases with prediction confidence levels that were close between classes. These findings confirm that VGG16-based CNNs are effective for accurate and efficient detection of rice leaf diseases. The application of this model has the potential to support faster decision-making, reduce pesticide use, and encourage environmentally friendly sustainable agricultural practices.
Co-Authors Agustinus Eko Setiawan Ahlun Nazar, Ahmad Ahmad Jurnaidi Wahidin Alfina Alfina Andika, Tahta Herdian Aras, Irsan Ardian, Odi Arifin, Yulyani Bintoro, Panji Dinda Rahmadanty Dwi Yana Ayu Andini Eko Setiawan, Agustinus Fahlul Rizki Fandi Ahmad Ferly Ardhy Fitra Endi Fernanda, Fitra Endi Fitri Hardiyanti, Fitri Gustianing, Defy Habib, Cahya Isti Fada, Nurul Muhammad, Adamu Abubakar Mukaromah, Hafsah Nur Aminudin Ockhy Jey Fhiter Wassalam Pasaribu, Alfredo Prayoga, Sahrul Rendy Yudha Pratama Salman Alfarisi Salimu Setiawan, Agustinus Eko Silvyana, Dety Sunarmi Syaputra, Andhika Tahta Herdian Andika Ubaidillah, Ilham Ulfa Isni Kurnia Wahyuni, Nuci Widiandana, Panggah Zulkifli