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All Journal ComEngApp : Computer Engineering and Applications Journal E-Dimas: Jurnal Pengabdian kepada Masyarakat Sistemasi: Jurnal Sistem Informasi Jurnal Informatika Jurnal Ilmiah FIFO Jurnal Pengabdian Pada Masyarakat InComTech: Jurnal Telekomunikasi dan Komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) IJISCS (International Journal Of Information System and Computer Science) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Asia International Conference on Industrial Revolution for Polytechnic Education Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Systems Engineering and Information Technology Kapas: Kumpulan Artikel Pengabdian Masyarakat IRA Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Nasional
Yudo Devianto
Universitas Mercu Buana
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Other

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Journal : Jurnal Sisfokom (Sistem Informasi dan Komputer)

 1 
Detection of Rice Leaf Pests Based on Images with Convolution Neural Network in Yollo v8 Fauzi, Ahmad; Baihaqi, Kiki Ahmad; Pertiwi, Anggun; Devianto, Yudo; Dwiasnati, Saruni
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.2008

Abstract

Detection of rice leaf pests is important in agriculture because it can help farmers determine appropriate preventive measures. One method that can be used to detect rice leaf pests is digital image processing technology. In this research, proof of suitability for solving this case was carried out between the Convolutional Neural Network (CNN) algorithm which was run offline with R-CNN and YOLOv8 for detecting rice leaf pests. At the data preparation stage, images of rice leaves were taken from various sources with a total of 100 images taken from website data and 10 images taken from the research site. Next, preprocessing and data augmentation are carried out to improve image quality and increase data variation. At the model training stage, a training and evaluation process is carried out using two types of algorithms, namely R-CNN and YOLOv8. The accuracy of the testing results using the same data using Yolov8 obtained 87.0% accuracy and 79% precision, while using R-CNN the results obtained were 85% for accuracy and 75% for precision with data divided into 80 training data 20 validation data and 10 testing data. Labeling the dataset uses Makesensei which has been completely standardized, with the resulting parameters being the spots on rice leaves.
Detection of Rice Leaf Pests Based on Images with Convolution Neural Network in Yollo v8 Fauzi, Ahmad; Baihaqi, Kiki Ahmad; Pertiwi, Anggun; Devianto, Yudo; Dwiasnati, Saruni
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.2008

Abstract

Detection of rice leaf pests is important in agriculture because it can help farmers determine appropriate preventive measures. One method that can be used to detect rice leaf pests is digital image processing technology. In this research, proof of suitability for solving this case was carried out between the Convolutional Neural Network (CNN) algorithm which was run offline with R-CNN and YOLOv8 for detecting rice leaf pests. At the data preparation stage, images of rice leaves were taken from various sources with a total of 100 images taken from website data and 10 images taken from the research site. Next, preprocessing and data augmentation are carried out to improve image quality and increase data variation. At the model training stage, a training and evaluation process is carried out using two types of algorithms, namely R-CNN and YOLOv8. The accuracy of the testing results using the same data using Yolov8 obtained 87.0% accuracy and 79% precision, while using R-CNN the results obtained were 85% for accuracy and 75% for precision with data divided into 80 training data 20 validation data and 10 testing data. Labeling the dataset uses Makesensei which has been completely standardized, with the resulting parameters being the spots on rice leaves.
Co-Authors Aditya Wibowo Ahmad Fauzi Andika Bayu Shantya Budi ARIF, SUTAN MOHAMMAD Avrizal, Reza Baihaqi, Kiki Ahmad Bambang Sukowo Bambang Sukowo Corry Coriah Haerudin Dwiasnati, Saruni Dwiki Jatikusumo Eliyani Eliyani Eugenius Kau Suni Fauzi Ahmad Muda Galih Priambodo Galih Priyo Waseso Gugun Gunawan Herry Derajad Wijaya Hidayat, Rahmat Rian Inda Wulan Irawati Khatin Faisah Muhammad Reza Ramdani Nur Septiyani Aglis Paradilla Shabilla Pertiwi, Anggun Popy Yuliarty, Popy Ratna Mutu Manikam Saputra, Dony Ramadhan Sari, Anggi Puspita Sari, Yunita Sartika Saruni Dwiasnati Saruni Dwiasnati Saruni Dwiasnati Saruni Dwiasnati Saruni Dwiasnati Sony Wirdianto Saputra Sukowo, Bambang Sumarto, Marco Alfan Wawan Gunawan Wawan Gunawan Wawan Gunawan