Texture Analysis of Lombok Songket Fabric Image Using Feature Extraction Method
Keywords:
GLCM, Texture analysis, Correlation coefficient, songket Lombok cloth, AnalysisAbstract
Songket cloth is a culture that must be preserved, Lombok is one area that has a center for songket craftsmen, songket fabrics in Lombok have various types, motifs and textures according to their respective regions. In this study, texture analysis was carried out to obtain the value between one songket cloth and another. The method used in this research is the feature extraction method of GLCM and Correlation Coefficient. The results of this study were the songket from Sade produced the same results between songket keker - songket pengginang = 0.987, songket keker - songket rarang = 0.96 and songket keker -ongket subhanale = 0.977. Whereas for the songket cloth originating from Pringgasela, the value between songket anteng - songket sempara rarely = -0.817, songket anteng -songket blue line = 0.959 and anteng songket -songket antique = 0.441. The Sukarara songket cloth produces the display value between the Sukarara songket keker - subhanale songket = -0.706, the songket keker voluntary - songket wayang = -0.892. The conclusion is that the proposed method can provide good feature extraction results.
References
I. Amalia, "Ekstraksi Fitur Citra Songket Berdasarkan Tekstur Menggunakan Metode Gray Level Co-occurrence Matrix (GLCM)," J. Infomedia, vol. 3, no. 2, pp. 64–68, 2018, doi: 10.30811/jim.v3i2.715.
B. Pathak and D. Barooah, "Texture analysis based on the Gray-level co-occurrence Matrix considering possible Orientations," Int. J. Adv. Res. Electr. Electron. Instrum. Eng., vol. 2, no. 9, pp. 4206–4212, 2013.
P. Mohanaiah, P. Sathyanarayana, and L. Gurukumar, "Image Texture Feature Extraction Using GLCM Approach," Int. J. Sci. Res. Publ., vol. 3, no. 5, pp. 1–5, 2013.
R. Azhar, D. Tuwohingide, D. Kamudi, Sarimuddin, and N. Suciati, "Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine," Procedia Comput. Sci., vol. 72, pp. 24–30, 2015, doi: 10.1016/j.procs.2015.12.101.
Yuhandri, S. Madenda, E. P. Wibowo, and karmila@staff gunadarma ac id Karmilasari, "Object feature extraction of songket image using chain code algorithm," Int. J. Adv. Sci. Eng. Inf. Technol., vol. 7, no. 1, pp. 235–241, 2017, doi: 10.18517/ijaseit.7.1.1479.
A. E. Minarno, A. Kurniawardhani, and F. Bimantoro, "Image retrieval based on multi structure co-occurrence descriptor," Telkomnika (Telecommunication Comput. Electron. Control., vol. 14, no. 3, pp. 1175–1182, 2016, doi: 10.12928/TELKOMNIKA.v14i3.3292.
B. Imran and M. M. Efendi, "THE IMPLEMENTATION OF EXTRACTION FEATURE USING GLCM AND BACK-PROPAGATION ARTIFICIAL NEURAL NETWORK TO CLASIFY LOMBOK SONGKET WOVEN CLOTH," J. Techno Nusa Mandiri, vol. 17, no. 2, pp. 131–136, 2020.
Yuhandri, S. Madenda, E. P. Wibowo, and Karmilasari, "Pattern recognition and classification using backpropagation neural network algorithm for songket motifs image retrieval," Int. J. Adv. Sci. Eng. Inf. Technol., vol. 7, no. 6, pp. 2343–2349, 2017, doi: 10.18517/ijaseit.7.6.2200.
B. Imran, K. Gunawan, M. Zohri, and L. D. Bakti, "Fingerprint pattern of matching family with GLCM feature," Telkomnika (Telecommunication Comput. Electron. Control., vol. 16, no. 4, 2018, doi: 10.12928/TELKOMNIKA.v16i4.8534.
Suharjito, B. Imran, and A. S. Girsang, "Family relationship identification by using extract feature of gray level co-zoccurrence matrix (GLCM) based on parents and children fingerprint," Int. J. Electr. Comput. Eng., vol. 7, no. 5, 2017, doi: 10.11591/ijece.v7i5.pp2738-2745.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
