Klasifikasi Jenis Beras Menggunakan Deep Learning Berbasis Computer Vision dengan Platform Roboflow
DOI:
https://doi.org/10.52958/jsia.v4i1.13175Abstract
Rice is a fundamental food commodity globally, where accurate variety classification is crucial for pricing, quality control, and food security. Manual classification methods are labor-intensive, time-consuming, and prone to human subjectivity. This research proposes an automated classification system for five rice varieties (Arborio, Basmati, Ipsala, Jasmine, and Karacadag) using a Computer Vision approach with Vision Transformer (ViT) architecture. Unlike Convolutional Neural Networks (CNN) which focus on local features, ViT utilizes selfattention mechanisms to capture global contextual relationships within images. The model was developed using the public "Rice Image Dataset" containing 75,000 images. The methodology includes image preprocessing (resizing and normalization) and training of the ViT Classification model. Model performance was evaluated using standard metrics on a separate test set. The results show that the proposed ViT model achieved an outstanding accuracy of 99.9%. These
findings demonstrate that the Transformer-based approach is highly effective and efficient for automating rice variety identification,
offering a more robust solution compared to conventional methods.
References
D. M. H. Very Kurnia Bakti1, “Segmentasi Dan Perbaikan Citra Untuk Proses,” Mar. 2016.
Karnadi, “PENGEMBANGAN APLIKASI DIGITAL IMAGE PROCESSING DENGAN MICROSOFT VISUAL BASIC DIGITAL IMAGE PROCESSING APPLICATION DEVELOPMENT WITH MICROSOFT VISUAL BASIC,” 2018. [Online]. Available: http://jurnal.um-palembang.ac.id/index.php/digital
E. A. Awalludin, T. N. T. Arsad, and W. N. J. H. Wan Yussof, “A Review on Image Processing Techniques for Fisheries Application,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Jun. 2020. doi: 10.1088/1742-6596/1529/5/052031.
I. 2), D. 3), K. A. Y. 4) Drajat Indah Mawarni 1), “METODE DIGITAL IMAGE PROCESSING UNTUK MENENTUKAN DISTRIBUSI UKURAN DIAMETER GELEMBUNG UDARA PADA MICROGELEMBUNG GENERATOR,” 2023.
S. Ashillah, S. A. Gulo, Y. Rahmi, D. Yandra Niska, and S. Adventino Gulo, “Implementasi Algoritma Pengolahan Citra Digital untuk Perbaikan Kualitas Gambar.”
A. S. Sembiring, “KETIK : Jurnal Informatika Faatuatua Media Karya Perbaikan Citra Digital Menggunakan Metode Image Inpainting,” 2024. [Online]. Available: https://jurnal.faatuatua.com/index.php/KETIK
S. Zahrah, R. Saptono, and E. Suryani, Identifikasi Gejala Penyakit Padi Menggunakan Operasi Morfologi Citra. 2016.
J. Sains dan Teknologi, J. Jumadi, and D. Sartika, “PENGOLAHAN CITRA DIGITAL UNTUK IDENTIFIKASI OBJEK MENGGUNAKAN METODE HIERARCHICAL AGGLOMERATIVE CLUSTERING”.
S. N. Ria, M. Walid, and B. A. Umam, “Pengolahan Citra Digital Untuk Identifikasi Jenis Penyakit Kulit Menggunakan Metode Convolutional Neural Network (CNN),” Energy - Jurnal Ilmiah Ilmu-Ilmu Teknik, vol. 12, no. 2, pp. 9–16, Dec. 2022, doi: 10.51747/energy.v12i2.1118.
S. Putri Hati Siregar, Z. Lestari Nasution, A. Evioni, K. Azmi, I. Komputer, and S. Dan Tekhnologi, “Pemanfaatan Pengolahan Citra untuk Identifikasi Wajah pada Sistem Keamanan Indonesia,” Jurnal Ilmu Komputer Dan Teknologi Informasi, vol. 3, no. 2, 2025, doi: 10.61132/neptunus.v3i1.783.
S. Hazra, S. Karforma, A. Bandyopadhyay, A. Banerjee, and D. Chakraborty, “Deep Learning-Based Classification Of Rice Varieties: A Comprehensive Study,” 2023.
H. Zheng et al., “An android-smartphone application for rice panicle detection and rice growth stage recognition using a lightweight YOLO network,” Front Plant Sci, vol. 16, 2025, doi: 10.3389/fpls.2025.1561632.
