(Universitas Stikubank, Indonesia)(2) Wiwien Hadikurniawati
(Universitas Stikubank, Indonesia)(3) * Anindita Septiarini
(Mulawarman University, Indonesia)(4) Hamdani Hamdani
(Mulawarman University, Indonesia)*corresponding author
AbstractBatik is a sort of cultural heritage fabric that originated in many areas of Indonesia. It can be traced back to many different parts of Indonesia. Each region, particularly Semarang in Central Java, Indonesia, has its Batik design. Unfortunately, due to a lack of knowledge, not all residents can recognize the types of Semarang batik. Therefore, this study proposed an automated method for classifying Semarang batik. Semarang batik was classified into five categories according to this method: Asem Arang, Blekok Warak, Gambang Semarangan, Kembang Sepatu, and Semarangan. It is required to analyze the color features based on the color space to develop discriminative features since color was able to differentiate these batik patterns. Color features were produced based on the RGB, HSV, YIQ, and YCbCr color spaces. Four different kernels were used to feed these features into the Support Vector Machine (SVM) classifier: linear, polynomial, sigmoid, and radial basis functions. The experiment was carried out using a local dataset of 1000 batik images classified into five classes (each class contains 200 images). A cross-validation test with a k-fold value of 10 was performed to analyze the method. In each of the SVM Kernels, the results showed that the proposed method achieved an accuracy value of 100% by utilizing the YIQ color space, which was reliable throughout all the tests.
Keywordsfeature extraction; color moment; YIQ; SVM; cross validation
|
DOIhttps://doi.org/10.26555/ijain.v8i2.821 |
Article metricsAbstract views : 1688 | PDF views : 446 |
Cite |
Full Text Download
|
References
[1] E. Purnamasari, “The Introduction of Classic Batik Motif to the Community Through Game,” SISFORMA, vol. 4, no. 1, pp. 31–37, Oct. 2017, doi: 10.24167/sisforma.v4i1.1039.
[2] UNESCO, “Indonesia Batik : Evaluation of the nominations for inscription on the Representative List of the Intangible Cultural Heritage of Humanity,” 2009. [Online]. Available: unesco.org. [Accessed: 17-Feb-2022].
[3] A. C. Siregar and B. C. Octariadi, “Classification of Sambas Traditional Fabric ‘Kain Lunggi’ Using Texture Feature,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 13, no. 4, pp. 389–398, 2019, doi: 10.22146/ijccs.49782.
[4] A. R. Juwita and A. Solichin, “Batik Pattern Identification Using GLCM and Artificial Neural Network Backpropagation,” Proc. 3rd Int. Conf. Informatics Comput. ICIC 2018, pp. 1–6, 2018, doi: 10.1109/IAC.2018.8780412.
[5] B. R. Lidiawaty, M. Isa Irawan, and R. V. Hari Ginardi, “Image Pattern Verification Based on Seller’s Batik Solo Product Name Using SURF As A Texture Based Image Retrieval,” IES 2020 - Int. Electron. Symp. Role Auton. Intell. Syst. Hum. Life Comf., pp. 674–679, 2020, doi: 10.1109/IES50839.2020.9231950.
[6] I. Nurhaida, A. Noviyanto, R. Manurung, and A. M. Arymurthy, “Automatic Indonesian’s batik pattern recognition using SIFT approach,” Procedia Comput. Sci., vol. 59, pp. 567–576, 2015, doi: 10.1016/j.procs.2015.07.547.
[7] I. Soesanti and R. Syahputra, “Batik production process optimization using particle swarm optimization method,” J. Theor. Appl. Inf. Technol., vol. 86, no. 2, pp. 272–278, 2016. Available at: Google Scholar.
[8] Y. Gultom, A. M. Arymurthy, and R. J. Masikome, “Batik Classification using Deep Convolutional Network Transfer Learning,” J. Ilmu Komput. dan Inf., vol. 11, no. 2, pp. 59–66, 2018, doi: 10.21609/jiki.v11i2.507.
[9] I. Nurhaida, H. Wei, R. A. M. Zen, R. Manurung, and A. M. Arymurthy, “Texture fusion for batik motif retrieval system,” Int. J. Electr. Comput. Eng., vol. 6, no. 6, pp. 3174–3187, 2016, doi: 10.11591/ijece.v6i6.12049.
[10] A. H. Rangkuti, A. Harjoko, and A. Putra, “A Novel Reliable Approach for Image Batik Classification That Invariant with Scale and Rotation Using MU2ECS-LBP Algorithm,” Procedia Comput. Sci., vol. 179, no. 2019, pp. 863–870, 2021, doi: 10.1016/j.procs.2021.01.075.
[11] A. Kasim, M. Bakri, and A. Septiarini, “The Artificial Neural Networks (ANN) for Batik Detection Based on Textural Features,” in Proceedings of the Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 2019, 12 October 2019, Bandung, West Java, Indonesia, 2020, pp. 1–9, doi: 10.4108/eai.12-10-2019.2296538.
[12] T. Hu, Q. Xie, Q. Yuan, J. Lv, and Q. Xiong, “Design of ethnic patterns based on shape grammar and artificial neural network,” Alexandria Eng. J., vol. 60, no. 1, pp. 1601–1625, Feb. 2021, doi: 10.1016/j.aej.2020.11.013.
[13] 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.
[14] Nuraedah, M. Bakri, and A. A. Kasim, “Quadratic support vector machine for the bomba traditional textile motif classification,” Indones. J. Electr. Eng. Comput. Sci., vol. 11, no. 3, pp. 1004–1014, 2018, doi: 10.11591/ijeecs.v11.i3.pp1004-1014.
[15] H. Hamdani, A. Septiarini, A. Sunyoto, S. Suyanto, and F. Utaminingrum, “Detection of oil palm leaf disease based on color histogram and supervised classifier,” Optik (Stuttg)., vol. 245, pp. 1–15, Nov. 2021, doi: 10.1016/j.ijleo.2021.167753.
[16] R. RAJ and D. M. V Sudhamani, “Retrieval of Images using Combination of Features as Color, Color Moments and Hu Moments,” Adv. Image Video Process., vol. 7, no. 5, pp. 09–21, Nov. 2019, doi: 10.14738/aivp.75.7208.
[17] A. Septiarini, A. Sunyoto, H. Hamdani, A. A. Kasim, F. Utaminingrum, and H. R. Hatta, “Machine vision for the maturity classification of oil palm fresh fruit bunches based on color and texture features,” Sci. Hortic. (Amsterdam)., vol. 286, pp. 1–8, Aug. 2021, doi: 10.1016/j.scienta.2021.110245.
[18] A. Septiarini, R. Saputra, A. Tejawati, M. Wati, H. Hamdani, and N. Puspitasari, “Analysis of Color and Texture Features for Samarinda Sarong Classification,” in 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2021, pp. 102–107, doi: 10.1109/ISRITI54043.2021.9702797.
[19] W. Herulambang, M. N. Hamidah, and F. Setyatama, “Comparison of SVM And BPNN Methods in The Classification of Batik Patterns Based on Color Histograms And Invariant Moments,” in 2020 International Conference on Smart Technology and Applications (ICoSTA), 2020, pp. 1–4, doi: 10.1109/ICoSTA48221.2020.1570615583.
[20] W. Xiong, J. Xu, Z. Xiong, J. Wang, and M. Liu, “Degraded historical document image binarization using local features and support vector machine (SVM),” Optik (Stuttg)., vol. 164, pp. 218–223, Jul. 2018, doi: 10.1016/j.ijleo.2018.02.072.
[21] H. Hartono, O. S. Sitompul, T. Tulus, and E. B. Nababan, “Biased support vector machine and weighted-smote in handling class imbalance problem,” Int. J. Adv. Intell. Informatics, vol. 4, no. 1, pp. 21–27, Mar. 2018, doi: 10.26555/ijain.v4i1.146.
[22] M. Latah and L. Toker, “A novel intelligent approach for detecting DoS flooding attacks in software-defined networks,” Int. J. Adv. Intell. Informatics, vol. 4, no. 1, pp. 11–20, Mar. 2018, doi: 10.26555/ijain.v4i1.138.
[23] Y. Ben Salem and M. N. Abdelkrim, “Texture classification of fabric defects using machine learning,” Int. J. Electr. Comput. Eng., vol. 10, no. 4, pp. 4390–4399, 2020, doi: 10.11591/ijece.v10i4.pp4390-4399.
[24] T. R. Baitharu, S. K. Pani, and S. K. Dhal, “Comparison of Kernel selection for support vector machines using diabetes dataset,” J. Comput. Sci. Appl., vol. 3, no. 6, pp. 181–184, 2015. Available at: Google Scholar.
[25] E. A. B. Ibrahim et al., “Evaluation of texture feature based on basic local binary pattern for wood defect classification,” Int. J. Adv. Intell. Informatics, vol. 7, no. 1, pp. 26–36, Mar. 2021, doi: 10.26555/ijain.v7i1.393.
[26] A. Ç. Seçkin and M. Seçkin, “Detection of fabric defects with intertwined frame vector feature extraction,” Alexandria Eng. J., vol. 61, no. 4, pp. 2887–2898, Apr. 2022, doi: 10.1016/j.aej.2021.08.017.
[27] Z. Zhan, J. Zhou, and B. Xu, “Fabric defect classification using prototypical network of few-shot learning algorithm,” Comput. Ind., vol. 138, p. 103628, Jun. 2022, doi: 10.1016/j.compind.2022.103628.
[28] Z. Pourkaramdel, S. Fekri-Ershad, and L. Nanni, “Fabric defect detection based on completed local quartet patterns and majority decision algorithm,” Expert Syst. Appl., vol. 198, p. 116827, Jul. 2022, doi: 10.1016/j.eswa.2022.116827.
[29] M. Chen et al., “Improved faster R-CNN for fabric defect detection based on Gabor filter with Genetic Algorithm optimization,” Comput. Ind., vol. 134, p. 103551, Jan. 2022, doi: 10.1016/j.compind.2021.103551.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571 (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
andri.pranolo.id@ieee.org (publication issues)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0