ANALISA FITUR EKSTRAKSI CIRI DAN WARNA DALAM PROSES KLASIFIKASI KEMATANGAN BUAH RAMBUTAN BERBASIS K-NEAREST NEIGHBOR
Keywords:
K-NN, rambutan, klasifikasi, GLCM, HSV
Abstract
Klasifikasi citra buah rambutan leci, lengkeng, pulasan dan rambutan yang merupakan buah dalam stau spesies telah dilakukan. Klasifikasi buah rambutan menggunakan KNN saja atau fitur ekstraksi saja sudah pernah dilakukan. Dalam penelitian ini, proses klasifikasi kematangan buah rambutan dilakukan dengan K-NN berbasis fitur ekstraksi ciri dan warna dengan tujuan untuk meningkatkan akurasi klasifikasi citra. Terpilih ekstraksi ciri GLCM dan ekstraksi ciri warna HSV, dimana masing-maisng mempunyai keunggulan masing-masing. Berdasarkan 100 dataset citra dalam 4 kelas yaitu mentah, setengah matang, matang dan busuk, telah dilakukan percobaan bervariasi menggunakan sudut GLCM dari 00, 450, 900, 1350dan nilai K=1,3,5,7,9,11,13. Akurasi terbaik yang dihasilkan yaitu 97,5% pada K=1 dan 00. Sedangkan yang terendah pada K=13 dan 1350 dengan hasil 62,5%.
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References
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[3] C. Kumar, S. Chauhan, R. N. Alla, and H. Mounica gurram, “Classifications of citrus fruit using image processing -GLCM parameters,” in 2015 International Conference on Communications and Signal Processing (ICCSP), 2015, pp. 1743–1747.
[4] L. Fu et al., “Fast and accurate detection of kiwifruit in orchard using improved YOLOv3-tiny model,” Precis. Agric., no. 0123456789, 2020.
[5] 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 Neighbor,” J. Inform. J. Pengemb. IT, vol. 4, no. 1, pp. 1–6, 2019.
[6] C. E. Cabrera Ardila, L. Alberto Ramirez, and F. A. Prieto Ortiz, “Spectral analysis for the early detection of anthracnose in fruits of Sugar Mango (Mangifera indica),” Comput. Electron. Agric., vol. 173, no. March, p. 105357, Jun. 2020.
[7] K. Komal and Sonia, “GLCM Algorithm and SVM Classification Method for Orange Fruit Quality Assessment,” Int. J. Eng. Res. Technol., vol. 8, no. 09, pp. 697–703, 2019.
[8] K. Warman, L. A. Harahap, and P. Munir, “Identifikasi Kematangan Buah Jeruk Dengan Teknik Jaringan Syaraf Tiruan,” J. Rekayasa Pangan dan Pert, vol. 3, no. 2, pp. 248–253, 2015.
[9] D. Nursantika and F. R. Umbara, “Pengenalan Citra Buah Manggis Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation,” in Seminar Nasional Telekomunikasi dan Informatika, 2016, no. Selisik, pp. 182–184.
[10] Z.-L. He et al., “A method of green litchi recognition in natural environment based on improved LDA classifier,” Comput. Electron. Agric., vol. 140, pp. 159–167, Aug. 2017.
[11] O. R. Indriani, E. J. Kusuma, C. A. Sari, E. H. Rachmawanto, and D. R. I. M. Setiadi, “Tomatoes Classification Using K-NN Based on GLCM and HSV Color Space,” in International Conference on Innovative and Creative Information Technology (ICITech), 2017, pp. 1–6.
[12] A. Zubair and A. R. Muslikh, “Identifikasi jamur menggunakan metode k-nearest neighbor dengan ekstraksi ciri morfologi,” in Seminar Nasional Sistem Informasi, 2017, no. September, pp. 965–972.
[13] Y. Chen et al., “Variety identification of orchids using Fourier transform infrared spectroscopy combined with stacked sparse auto-encoder,” Molecules, vol. 24, no. 13, 2019.
[14] E. Hari Rachmawanto, G. Rambu Anarqi, D. R. I. Moses Setiadi, and C. Atika Sari, “Handwriting Recognition Using Eccentricity and Metric Feature Extraction Based on K-Nearest Neighbors,” Proc. - 2018 Int. Semin. Appl. Technol. Inf. Commun. Creat. Technol. Hum. Life, iSemantic 2018, pp. 411–416, 2018.
[15] I. U. W. Mulyono et al., “Parijoto Fruits Classification using K-Nearest Neighbor Based on Gray Level Co-Occurrence Matrix Texture Extraction,” J. Phys. Conf. Ser., vol. 1501, no. 1, 2020.
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[17] S. Jana, S. Basak, and R. Parekh, “Automatic fruit recognition from natural images using color and texture features,” in 2017 Devices for Integrated Circuit (DevIC), 2017, pp. 620–624.
[18] S. A. Banday and A. H. Mir, “Statistical textural feature and deformable model based brain tumor segmentation and volume estimation,” Multimed. Tools Appl., vol. 76, no. 3, pp. 3809–3828, 2017.
[19] E. Hossain, M. F. Hossain, and M. A. Rahaman, “A Color and Texture Based Approach for the Detection and Classification of Plant Leaf Disease Using KNN Classifier,” 2nd Int. Conf. Electr. Comput. Commun. Eng. ECCE 2019, pp. 1–6, 2019.
[20] F. Ferreira Lima dos Santos, J. Rosas, R. Martins, G. Araújo, L. Viana, and J. Gonçalves, “Quality assessment of coffee beans through computer vision and machine learning algorithms,” Coffee Sci., vol. 15, no. 1, pp. 1–9, 2020.
[21] P. N. Andono, E. H. Rachmawanto, N. S. Herman, and K. Kondo, “Orchid types classification using supervised learning algorithm based on feature and color extraction,” Bull. Electr. Eng. Informatics, vol. 10, no. 5, pp. 2530–2538, Oct. 2021.
[22] S. Sanjaya, M. L. Pura, S. K. Gusti, F. Yanto, and F. Syafria, “K-Nearest Neighbor for Classification of Tomato Maturity Level Based on Hue, Saturation, and Value Colors,” Indones. J. Artif. Intell. Data Min., vol. 2, no. 2, p. 101, Nov. 2019.
[23] A. Susanto, Z. H. Dewantoro, C. A. Sari, D. R. I. M. Setiadi, E. H. Rachmawanto, and I. U. W. Mulyono, “Shallot Quality Classification using HSV Color Models and Size Identification based on Naive Bayes Classifier,” J. Phys. Conf. Ser., vol. 1577, no. 1, 2020.
Published
2022-07-26
How to Cite
[1]
H. Hadi and E. Rachmawanto, “ANALISA FITUR EKSTRAKSI CIRI DAN WARNA DALAM PROSES KLASIFIKASI KEMATANGAN BUAH RAMBUTAN BERBASIS K-NEAREST NEIGHBOR”, SKANIKA, vol. 5, no. 2, pp. 177-189, Jul. 2022.
Section
Articles
