DETEKSI KUALITAS BERAS MENGGUNAKAN SEGMENTASI CITRA BERDASARKAN PECAHAN BULIR DAN SEBARAN WARNA
Keywords:
kualitas beras; citra beras; segmentasi Otsu; K-Nearest Neigbour;Abstract
Ada banyak kasus penipuan dalam pemalsuan beras dengan mencampur beras berkualitas baik dengan beras berkualitas rendah untuk kenaikan harga. Untuk melindungi masyarakat dari pemalsuan, kami melakukan penelitian untuk mendeteksi kualitas beras yang nantinya dapat membantu masyarakat untuk dapat membedakan kualitas baik dan buruk. Penelitian ini menyajikan sistem pemrosesan citra beras berbiaya rendah untuk menilai kualitas beras. Banyak faktor yang mempengaruhi kualitas beras seperti fragmen biji-bijian, warna yang tidak seragam, bau dan faktor lainnya. Penelitian ini menggunakan prosentase butiran beras pecah dan keseragaman warna untuk menentukan kualitas beras. Kami mengusulkan fitur tekstur dengan segmentasi Otsu untuk menentukan jumlah butiran pecah dan distribusi warna untuk menentukan seragam warna. Hasil klasifikasi menggunakan validasi K Folddengan k=10 pada data asli menunjukkan hasil K-Nearest Neigbour memiliki akurasi 99,87%.
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[7] S. Shantaiya and U. Ansari, “Identification Of Food Grains And Its Quality Using Pattern Classification,†Image (Rochester, N.Y.), vol. 2, no. 2, pp. 70–74, 2010.
[8] G. Van Dalen, “Determination of the size distribution and percentage of broken kernels of rice using flatbed scanning and image analysis,†Food Res. Int., vol. 37, no. 1, pp. 51–58, 2004.
[9] H. S. Gujjar, “A Method for Identification of Basmati Rice grain of India and Its Quality Using Pattern Classification,†vol. 3, no. 1, pp. 268–273, 2013.
[10] D. Sharma and S. D. Sawant, “Grain quality detection by using image processing for public distribution,†Proc. 2017 Int. Conf. Intell. Comput. Control Syst. ICICCS 2017, vol. 2018–January, pp. 1118–1122, 2018.
[11] P. Kongsawat, “Quality Assessment of Thai Rice Kernels Using Low Cost Digital Image Processing System,†2018.
[12] Nagoda, N., & Ranathunga, L. (2018), “Rice Sample Segmentation and Classification Using Image Processing and Support Vector Machine,†2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS), (978), 179–184. https://doi.org/10.1109/ICIINFS.2018.8721312
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[14] Parveen, E. Z. (2017), “Assessment of Quality of Rice Grain using Optical and Image Processing Technique,†265–270.
[15] Ruslan, R., & Ibrahim, M. F. (2018), “Effect of Background Color on Rice Seed Image Segmentation using Machine Vision,†2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA),1–4.
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2020-08-19
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Supriyadi E, Basuki A, Sigit R. DETEKSI KUALITAS BERAS MENGGUNAKAN SEGMENTASI CITRA BERDASARKAN PECAHAN BULIR DAN SEBARAN WARNA. JIRE [Internet]. 2020 Aug. 19 [cited 2025 Jul. 11];3(1):20-9. Available from: http://e-journal.stmiklombok.ac.id/index.php/jire/article/view/210
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