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All Journal Jurnal Algoritma Jurnal Informatika dan Teknik Elektro Terapan PROCEEDING IC-ITECHS 2014 SMARTICS Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTECOMS: Journal of Information Technology and Computer Science Jiko (Jurnal Informatika dan komputer) KOMPUTIKA - Jurnal Sistem Komputer JIKA (Jurnal Informatika) Infotek : Jurnal Informatika dan Teknologi Infotech: Journal of Technology Information SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Algoritma Proceeding of International Conference Health, Science And Technology (ICOHETECH) Innovative: Journal Of Social Science Research Journal of Software Engineering and Information System (SEIS) SmartComp
Pradana, Afu Ichsan
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Other

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Optimalisasi Akurasi Model Identifikasi Penyakit Pada Daun Padi Dengan Fine-Tuning YOLOv11 Untuk Ketahanan Pangan Berkelanjutan Harsanto; Pradana, Afu Ichsan; Wahyu Pamekas, Bondan
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.22-2.2945

Abstract

Rice is one of Indonesia's main food commodities, whose productivity often declines due to leaf disease. Early detection of rice leaf disease is an important aspect of maintaining sustainable food security. This study aims to optimize the accuracy of early identification of rice leaf disease by fine-tuning the YOLOv11 model. The research stages included dataset collection, annotation, data preprocessing, data augmentation, model training, fine-tuning, and model performance evaluation. The results showed an improvement in model performance after fine-tuning, with the overall recall value increasing from 0.760 to 0.788 and mAP from 0.764 to 0.785. The confusion matrix also shows a more stable prediction distribution in the fine-tuned model compared to the initial model. Thus, fine-tuning YOLOv11 has proven to be effective in improving the accuracy of early identification of rice leaf diseases and has the potential to support the application of artificial intelligence in the agricultural sector to strengthen food security in Indonesia.
Co-Authors Aboobaider, Burhanuddin bin Mohd Aji, Sindhu Purnomo Akmal, Baasith Khoiruddin arsanto, arsanto Ashidiqi, Andhyka Yoga Atina, Vihi Azizah, Shelvi Bagaskara, Aditya Dwi Hartanti Eko Purwanto Harjanto, Prabandaru Pramu Harsanto Harsanto, Harsanto Harsanto, Malika Indriyani, Tiara Insaan, Nashiirotul Jordan, Alles Tio Junantoro, Hagai Kusuma, Adri Surya Maulindar, Joni Nugroho, Nasrun Nurchim Nurchim Pamekas, Bondan Wahyu Permana, Aditya Candra Permatasari, Hanifah Pipin Widyaningsih Pratama, Fandi Aziz Pujiati Edy Santoso, Elysa Mei Putra, Enggar Wijaya Putra, Wihan Perkasa Nugraha Rakadipa, Dhimas Arya Rengga, Nikodemos Rifky, Raja Saputra, Ilham Tristadika Setiawan, Gilang Sopingi Sopingi, Sopingi Tory, Alfa Rado Andre Yusa Saka Wahyu Pamekas, Bondan Wahyudi Wahyudi Wijanarko, Restu Gilang Wiji Lestari Wijiyanto Wijiyanto, Wijiyanto Yusuf Maulana, Yusuf