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All Journal International Journal of Community Service Bulletin of Computer Science Research Bureaucracy Journal : Indonesia Journal of Law and Social-Political Governance Jurnal Ilmu Komputer JURNAL KESEHATAN, SAINS, DAN TEKNOLOGI (JAKASAKTI) Jurnal Sains dan Teknologi Informasi Jurnal Ilmu Komputer, Teknologi Dan Informasi
Syahrial
Universitas Muhammadiyah Gorontalo
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Public Health Social Sciences Other

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Penerapan Convolutional Neural Network Berbasis Computer Vision untuk Identifikasi Varietas Padi Berdasarkan Citra Bulir Gabah Fachriel A. Pomalingo; Syahrial Syahrial; Mohamad Ilyas Abas; Irawan Ibrahim
Jurnal Sains dan Teknologi Informasi Vol 5 No 4 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jussi.v5i4.11227

Abstract

Rice variety identification based on paddy grain images faces a challenge because several varieties have relatively similar visual characteristics, particularly in shape, size, and color, which may lead to inconsistent manual identification. This condition indicates the need for a computer vision approach that can automatically learn the visual characteristics of paddy grains. This study aimed to develop a Convolutional Neural Network (CNN) model to identify five rice varieties available at the Agricultural Research and Modernization Center of Gorontalo (BRMP Gorontalo), namely Ciherang, Inpari 30, Inpari 42, Inpari IR Nutri Zinc, and Mekongga. The dataset was obtained through direct image acquisition, consisting of 28 RAW photographs for each variety and nine cropped images from each photograph, resulting in 1,260 images. The research stages included preprocessing, Custom CNN development, evaluation of image size, learning rate, number of convolutional layers, and data splitting, as well as comparison with MobileNetV2, ResNet50, DenseNet121, and InceptionV3. The best configuration used an input size of 224×224 pixels, a learning rate of 0.0005, four convolutional layers, and an 80:20 data split. Testing on 135 final testing images achieved 93.33% accuracy, 93.44% precision, 93.33% recall, and 93.32% F1-score. These results indicate that the Custom CNN effectively identified the five rice varieties under the dataset and experimental conditions used in this study.
Co-Authors Agus Hakri Bokingo Fachriel A. Pomalingo Firmansyah Pipii Haris Danial Ibrahim, Irawan Irawan Ibrahim Mohamad Ilyas Abas Pranata, Widya Eka Rahman M. Abdullah Rizal Lamusu Rizky Sahril Djaina Salahudin Pakaya Silvana Yusuf Tahir Supu Verliana Kiayi Wahyudin Hasyim Widya Eka Pranata Widya Eka Pranata