Olief Ilmandira Ratu Farisi, Gulpi Qorik Oktagalu Pratamasunu, Siti Sulaihah


The distribution of mixed rice on the market makes it difficult for consumers to determine the rice quality. In determining rice quality, the consumers consider and compare the texture, size and shape, color, aroma, purity, and homogeneity manually. This process is prone to errors and mistakes, due to the limited ability of each human's vision. Therefore, a method to determine the quality of rice automatically based on the physical characteristics of rice is needed. In this paper, we proposed an automatic rice quality classification method using backpropagation based on the shape and color of the rice. There are four parameters used to determine the classification process, namely compactness, circularity, mean, and skewness. Compactness and circularity were used to determine the ratio between the whole rice and broken rice. While mean and skewness were used to determine the color distribution of the rice. Experiments have been performed on 100 images consisting of 50 premium and 50 medium rice images. The experimental results show that the proposed method can classify rice based on its shape and color effectively with an accuracy rate of 95%.


rice quality, classification, backpropagation, shape feature, color feature

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D. D. Handoko, “Seputar Mutu Beras Kemasan dan Pencampuran Beras,” Ministry of Agriculture Republic Indonesia. (accessed Jul. 26, 2021).

M. C. Custodio, R. P. Cuevas, J. Ynion, A. G. Laborte, M. L. Velasco, and M. Demont, “Rice quality: How is it defined by consumers, industry, food scientists, and geneticists?,” Trends Food Sci. Technol., vol. 92, pp. 122–137, Oct. 2019, doi: 10.1016/J.TIFS.2019.07.039.

N. Hong Son and N. Thai-Nghe, “Deep Learning for Rice Quality Classification,” Proc. - 2019 Int. Conf. Adv. Comput. Appl. ACOMP 2019, pp. 92–96, Nov. 2019, doi: 10.1109/ACOMP.2019.00021.

F. N. Fajri, N. Hamid, and R. A. Pramunendar, “The recognition of mango varieties based on the leaves shape and texture using back propagation neural network method,” Proc. - 2017 Int. Conf. Sustain. Inf. Eng. Technol. SIET 2017, vol. 2018-January, pp. 14–20, Feb. 2018, doi: 10.1109/SIET.2017.8304101.

BSN, SNI 6128:2015 Uji Mutu Beras. Jakarta: Badan Standardisasi Nasional, 2015.

G. Kumar and P. K. Bhatia, “A detailed review of feature extraction in image processing systems,” Int. Conf. Adv. Comput. Commun. Technol. ACCT, pp. 5–12, 2014, doi: 10.1109/ACCT.2014.74.

W. K. Mutlag, S. K. Ali, Z. M. Aydam, and B. H. Taher, “Feature Extraction Methods: A Review,” J. Phys. Conf. Ser., vol. 1591, no. 1, p. 012028, Jul. 2020, doi: 10.1088/1742-6596/1591/1/012028.

M. Ravichandran, D., Nimmatoori, R., Ashwin Dhivakar, “A study on Image Statistics and Image Features on Coding Performance of Medical Images,” Int. J. Adv. Comput. Eng. Commun. Technol., vol. 5, no. 1, pp. 1–6, 2016.

F. D. Syahfitra, R. Syahputra, and K. T. Putra, “Implementation of Backpropagation Artificial Neural Network as a Forecasting System of Power Transformer Peak Load at Bumiayu Substation,” J. Electr. Technol. UMY, vol. 1, no. 3, pp. 118–125, Dec. 2017, doi: 10.18196/JET.1316.

M. Athoillah, M. I. Irawan, and E. M. Imah, “STUDY COMPARISON OF SVM-, K-NN- AND BACKPROPAGATION-BASED CLASSIFIER FOR IMAGE RETRIEVAL,” J. Ilmu Komput. dan Inf., vol. 8, no. 1, pp. 11–18, Mar. 2015, doi: 10.21609/JIKI.V8I1.279.


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