Klasifikasi Citra Mengkudu Berdasarkan Perhitungan Jarak Piksel pada Algoritma K-Nearest Neighbour

  • Candra Irawan Universitas Dian Nuswantoro
  • Eko Hari Rachmawanto Universitas Dian Nuswantoro
  • Christy Atika Sari Universitas Dian Nuswantoro
  • Raisul Umah Nur Universitas Dian Nuswantoro
Abstract views: 177 , PDF downloads: 219
Keywords: k-nearest neighbour;, noni image;, classification;, hue saturation value;, grey level co-occurrence, matrix;

Abstract

Noni fruit is included in exported food commodities in Indonesia. The size of noni fruit, based on human vision, generally has varied shapes with distinctive textures and various patterns, so that the process of filtering fruit based on color and shape can be done in large quantities. In this study, K-Nearest Neighbor (KNN) has been implemented as a classification algorithm because it has advantages in classifying images and is resistant to noise. Noni imagery is a personal image taken from a noni garden in the morning and undergoes a background subtraction process. The imagery quality improvement technique uses the Hue Saturation Value (HSV) color feature and the Gray Level Co-Occurrence Matrix (GLCM) characteristic feature. KNN accuracy without features is lower than using HSV and GLCM features. From the experimental results, the highest accuracy was obtained using HSV-GLCM at K is 1 and d is 1, namely 95%, while the lowest accuracy was 55% using KNN only at K is 5 and d is 8.

References

S. F. Kusuma, R. E. Pawening, and R. Dijaya, “Otomatisasi Klasifikasi Kematangan Buah Mengkudu Berdasarkan Warna Dan Tekstur,” Regist. J. Ilm. Teknol. Sist. Inf., vol. 3, no. 1, pp. 17–23, 2017.

I. Tarsono, D. Triyanto, and T. Rismawan, “Prototype Pemisah Otomatis Jeruk Siam Berdasarkan Warna Menggunakan Metode KNN (K-Nearest Neighbour),” J. Coding, Sist. Komput. Untan, vol. 06, no. 1, pp. 44–53, 2018.

S. Y. R. Riska and P. Subekti, “Klasifikasi Level Kematangan Buah Tomat Berdasarkan Fitur Warna Menggunakan Multi-Svm,” J. Ilm. Inform., vol. 1, no. 1, 2016.

F. Wibowo, D. K. Hakim, and S. Sugiyanto, “Pendugaan Kelas Mutu Buah Pepaya Berdasarkan Ciri Tekstur Glcm Menggunakan Algoritma K-Nearest Neighbour,” J. Nas. Pendidik. Tek. Inform., vol. 7, no. 1, p. 100, 2018.

E. Budianita, J. Jasril, and L. Handayani, “Implementasi Pengolahan Citra dan Klasifikasi K-Nearest Neighbour Untuk Membangun Aplikasi Pembeda Daging Sapi dan Babi Berbasis Web,” J. Sains dan Teknol. Ind., vol. 12, no. Vol 12, No 2 (2015): Juni 2015, pp. 242–247, 2015.

R. A. Surya, A. Fadlil, A. Yudhana, M. T. Informatika, P. T. Informatika, and U. A. Dahlan, “Ekstraksi Ciri Metode Gray Level Co-Occurrence Matrix ( GLCM ) dan Filter Gabor Untuk Klasifikasi Citra Batik Pekalongan,” vol. 02, no. 02, pp. 23–26, 2017.

J. D.Pujari, R. Yakkundimath, and A. S. Byadgi, “SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique,” Int. J. Interact. Multimed. Artif. Intell., vol. 3, no. 7, p. 6, 2016.

Y. Ji, Q. Zhao, S. Bi, and T. Shen, “Apple Grading Method Based on Features of Color and Defect,” in Chinese Control Conference, 2018, pp. 5364–5368.

M. S. M. Alfatni, A. R. Mohamed Shariff, S. K. Bejo, O. M. Ben Saaed, and A. Mustapha, “Real-time oil palm FFB ripeness grading system based on ANN, KNN and SVM classifiers,” IOP Conf. Ser. Earth Environ. Sci., vol. 169, no. 1, p. 012067, Jul. 2018.

S. Zhang, X. Wu, S. Zhang, Q. Cheng, and Z. Tan, “An effective method to inspect and classify the bruising degree of apples based on the optical properties,” Postharvest Biol. Technol., vol. 127, pp. 44–52, 2017.

M. S. Hossain, M. Al-Hammadi, and G. Muhammad, “Automatic Fruit Classification Using Deep Learning for Industrial Applications,” IEEE Trans. Ind. Informatics, vol. 15, no. 2, pp. 1027–1034, Feb. 2019.

F. Wibowo and A. Harjoko, “Klasifikasi Mutu Pepaya Berdasarkan Ciri Tekstur GLCM Menggunakan Jaringan Saraf Tiruan,” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 3, no. 2, p. 100, Jan. 2018.

M. A. Anggriawan, M. Ichwan, and D. B. Utami, “Pengenalan Tingkat Kematangan Tomat Berdasarkan Citra Warna Pada Studi Kasus Pembangunan Sistem Pemilihan Otomatis,” J. Tek. Inform. dan Sist. Inf., vol. 3, no. 3, pp. 550–564, 2017.

R. Setyawan, M. A. Almahfud, C. A. Sari, D. R. I. M. Setiadi, and E. H. Rachmawanto, “MRI Image Segmentation using Morphological Enhancement and Noise Removal based on Fuzzy C-means,” in 2018 5th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 2018, pp. 99–104.

Y. Tian, E. Li, L. Yang, and Z. Liang, “An image processing method for green apple lesion detection in natural environment based on GA-BPNN and SVM,” Proc. 2018 IEEE Int. Conf. Mechatronics Autom. ICMA 2018, pp. 1210–1215, 2018.

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.

C. Paramita, E. Hari Rachmawanto, C. Atika Sari, and D. R. Ignatius Moses Setiadi, “Klasifikasi Jeruk Nipis Terhadap Tingkat Kematangan Buah Berdasarkan Fitur Warna Menggunakan K-Nearest Neighbour,” J. Inform. J. Pengemb. IT, vol. 4, no. 1, pp. 1–6, 2019.

D. Yulianto, R. N. Whidhiasih, and Maimunah, “Klasifikasi Tahap Kematangan Pisang Ambon Berdasarkan Warna Menggunakan Naive Bayes,” PIKSEL Penelit. Ilmu Komput. Sist. Embed. Log., vol. 5, no. 2, pp. 60–67, 2017.

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Published
2023-07-29