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Classification of Tangerine (Citrus Reticulata Blanco) Quality Using Combination of GLCM, HSV, and K-NN
https://doi.org/10.22146/ijccs.47906 Friska Ayu Listya(1*), Nur Rokhman(2)
(1) Master Program of Computer Science; FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta, Indonesia
(*) Corresponding Author
Abstract
The quality of fruit production is very important because it is related to the value of sales. Data from the Directorate General of Horticulture at the Ministry of Agriculture in 2017 showed that 94,3% of the total yield of citrus fruits is a type of tangerine. In the classification of the quality, the visual observation process is strongly influenced by subjectivity so that in certain conditions such as tired eyes and the number of oranges that want to classify too many the process can be inconsistent and also take a long time. Therefore, a technology is needed to accelerate the classification process and make it more objective. This study combines the Gray level Co-occurrence Matrix (GLCM) method for texture, Hue, Saturation, Value (HSV) features for color features and the k-Nearest Neighbor (k-NN) classification method. The data used were 60 images of rotten tangerines and 60 images of not rotten tangerines divided using a 4-fold cross-validation method to find the best combination of data training and data testing. 3 main processes will be carried out, namely preprocessing, feature extraction and classification. This study produced the highest accuracy of 80% from the combined of GLCM and HSV features extraction with value k = 5 for k-NN .
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[1] K. P. D. J. Hortikultura, Statistik Pertanian 2018. Jakarta: Pusat Data dan Sistem Informasi Pertanian, Kementrian Pertanian Republik Indonesia, 2018. [Online]. Available: http://epublikasi.setjen.pertanian.go.id/download/file/438-statistik-pertanian-2018 [Accessed: 10-Aug-2019]
[2] R. Widodo, A. W. Widodo, and A. Supriyanto, “Pemanfaatan Ciri Gray Level Co-Occurrence Matrix ( GLCM ) Citra Buah Jeruk Keprok ( Citrus reticulata Blanco ) untuk Klasifikasi Mutu,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 11, pp. 5769–5776, 2018. [Online]. Available: http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/3420/1336 [Accessed: 16-Jul-2019]
[3] C. Paramita, E. Hari Rachmawanto, C. Atika Sari, and M. S. Ignatius, “Klasifikasi Jeruk Nipis Terhadap Tingkat Kematangan Buah Berdasarkan Fitur Warna Menggunakan K-Nearest Neighbor,” J. Inform. J. Pengemb. IT, vol. 4, no. 1, pp. 1–6, 2019.
[4] O. R. Indriani, E. J. Kusuma, C. A. Sari, E. H. Rachmawanto, and D. R. Setiadi, “Tomatoes classification using K-NN based on GLCM and HSV color space,” Proc. - 2017 Int. Conf. Innov. Creat. Inf. Technol. Comput. Intell. IoT, ICITech 2017, vol. 2018-Janua, no. November, pp. 1–6, 2018. [Online]. Available: https://www.researchgate.net/publication/323861268_Tomatoes_classification_using_K-NN_based_on_GLCM_and_HSV_color_space [Accessed: 16-Jul-2019]
[5] P. Rianto and A. Harjoko, “Penentuan Kematangan Buah Salak Pondoh Di Pohon Berbasis Pengolahan Citra Digital,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 11, no. 2, p. 143, 2017. [Online]. Available: https://jurnal.ugm.ac.id/ijccs/article/view/17416/16693 [Accessed: 16-Jul-2019]
[6] Karimah F.U., Klasifikasi Citra Batik Kain Besurek Dengan Speed Up Robust Features (Surf) Dan Gray Level Co-Occurrence Matrix (Glcm). Yogyakarta, 2017.
[7] R. A. Rasool, “Iris Feature Extraction and Recognition based on Gray Level Co-occurrence Matrix ( GLCM ) Technique,” Int. J. Comput. Appl. (0975 – 8887), vol. Volume 181, no. January, 2018. [Online]. Available: https://www.researchgate.net/publication/329718619_Iris_Feature_Extraction_and_Recognition_based_on_Gray_Level_Co-occurrence_Matrix_GLCM_Technique [Accessed: 17-Jul-2019]
[8] S. Kolkur, D. Kalbande, P. Shimpi, C. Bapat, and J. Jatakia, “Human Skin Detection Using RGB, HSV and YCbCr Color Models,” ICCASP/ICMMD-2016. Adv. Intell. Syst. Res., vol. 137, pp. 324–332, 2017. [Online]. Available: https://arxiv.org/ftp/arxiv/papers/1708/1708.02694.pdf [Accessed: 17-Jul-2019]
[9] A. Z. Maula, C. Rahmad, and U. D. Rosiani, “Pengembangan Aplikasi Pemilihan Buah Tomat Untuk Bibit Unggul Berdasarkan Warna Dan Ukuran Menggunakan HSV Dan Thresholding,” J. Teknol. Inf., vol. 7, no. 2, pp. 127–138, 2016. [Online]. Available: http://ejurnal.stimata.ac.id/index.php/TI/article/view/223/253 [Accessed: 18-Jul-2019]
[10] A. Wada et al., “Differentiating Alzheimer’s Disease from Dementia with Lewy Bodies Using a Deep Learning Technique Based on Structural Brain Connectivity,” Magn. Reson. Med. Sci., vol. 18, no. 3, pp. 219–224, 2019. [Online]. Available: https://www.jstage.jst.go.jp/article/mrms/advpub/0/advpub_mp.2018-0091/_pdf/-char/en [Accessed: 18-Aug-2019]
[11] J. Aguilera, L. C. González, M. Montes-y-Gómez, and P. Rosso, “A new weighted k-nearest neighbor algorithm based on newton’s gravitational force,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11401 LNCS, no. November, pp. 305–313, 2019. [Online]. Available: https://www.researchgate.net/publication/329129843_A_New_Weighted_k-Nearest_Neighbor_Algorithm_based_on_Newton's_Gravitational_Force [Accessed: 18-Jul-2019]
[12] Akhyar, R. Hidayat, and B. Hantono, “Identifikasi Fitur Wajah Dengan Menggunakan Facial Points Dan Euclidean Distance,” Citee, pp. 154–160, 2017.
DOI: https://doi.org/10.22146/ijccs.47906
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