Improved Wavelet-GLCM for Robust Batik Motif Classification

https://doi.org/10.22146/ijccs.108011

Gregorius Adi Pradana(1), Agus Harjoko(2*)

(1) Master of Computer Science Study Program, Universitas Gadjah Mada
(2) Department of Computer Science and Electronics, Universitas Gadjah Mada
(*) Corresponding Author

Abstract


Batik is a traditional Indonesian fabric made by applying wax on the fabric, then processed with a certain technique. The diversity of batik motifs often makes it difficult for people to recognize them. Therefore, a batik motif classification system is needed, one of the methods of which is based on digital image processing. However, in this method, variations in rotation and scale in the image often cause feature values to change, thus decreasing accuracy. To overcome this, this study proposes a robust Improved Wavelet-GLCM (IWGLCM) feature extraction method for classifying batik motif images that account for rotation and scale variations. This method combines the Gray Level Co-occurrence Matrix (GLCM) and statistical values from the results of the Discrete Wavelet Transform (DWT). These combined features are then classified using Support Vector Machine (SVM). In the test scenario with variations in rotation and scale at the same time, this method managed to achieve optimal performance with an accuracy of 95.00%, a precision of 95.08%, a recall of 95.00%, and an f1-score of 95.00%.

Keywords


Batik Motif; Classification; Rotation and Scale Variation; IWGLCM; SVM

Full Text:

PDF


References

E. Winarno, W. Hadikurniawati, A. Septiarini, and H. Hamdani, “Analysis of color features performance using support vector machine with multi-kernel for batik classification,” International Journal of Advances in Intelligent Informatics, vol. 8, no. 2, 2022, doi: 10.26555/ijain.v8i2.821.

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,” in Procedia Computer Science, 2021. doi: 10.1016/j.procs.2021.01.075.

A. Fadlil, I. Riadi, and I. J. D. E. Purwadi Putra, “Comparison of Machine Learning Performance Using Naive Bayes and Random Forest Methods to Classify Batik Fabric Patterns,” Revue d’Intelligence Artificielle, vol. 37, no. 2, 2023, doi: 10.18280/ria.370214.

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, 2021. doi: 10.1109/ISRITI54043.2021.9702797.

A. E. Minarno, F. D. S. Sumadi, H. Wibowo, and Y. Munarko, “Classification of batik patterns using K-nearest neighbor and support vector machine,” Bulletin of Electrical Engineering and Informatics, vol. 9, no. 3, 2020, doi: 10.11591/eei.v9i3.1971.

A. E. Minarno, I. Soesanti, and H. A. Nugroho, “Batik Classification using Microstructure Co-occurrence Histogram,” International Journal on Informatics Visualization, vol. 8, no. 1, 2024, doi: 10.62527/joiv.8.1.2152.

F. U. Karimah and A. Harjoko, “Classification of batik kain besurek image using speed up robust features (SURF) and gray level co-occurrence matrix (GLCM),” in Communications in Computer and Information Science, 2017. doi: 10.1007/978-981-10-7242-0_7.

N. Suciati, D. Herumurti, and A. Y. Wijaya, “Feature extraction using gray-level co-occurrence matrix of wavelet coefficients and texture matching for batik motif recognition,” in Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 2017. doi: 10.1117/12.2266933.

B. J. Filia et al., “Improving Batik Pattern Classification using CNN with Advanced Augmentation and Oversampling on Imbalanced Dataset,” in Procedia Computer Science, 2023. doi: 10.1016/j.procs.2023.10.552.

S. Aras, A. Setyanto, and Rismayani, “Classification of Papuan Batik Motifs Using Deep Learning and Data Augmentation,” in 2022 4th International Conference on Cybernetics and Intelligent System, ICORIS 2022, 2022. doi: 10.1109/ICORIS56080.2022.10031320.

F. Budiman, A. Suhendra, D. Agushinta, and A. Tarigan, “Wavelet decomposition levels analysis for Indonesia traditional batik classification,” J Theor Appl Inf Technol, vol. 92, no. 2, 2016.

M. K. Nazir, M. A. N. U. Ghani, A. Ashraf, S. M. Alam, R. U. Farooq, and Z. Latif, “A Textile Image Classification based on Texture and Shape Features,” in 4th International Conference on Innovative Computing, ICIC 2021, 2021. doi: 10.1109/ICIC53490.2021.9693067.

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 Proceeding - ICoSTA 2020: 2020 International Conference on Smart Technology and Applications: Empowering Industrial IoT by Implementing Green Technology for Sustainable Development, 2020. doi: 10.1109/ICoSTA48221.2020.1570615583.



DOI: https://doi.org/10.22146/ijccs.108011

Article Metrics

Abstract views : 65 | views : 50

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2026 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs


View My Stats1
View My Stats2