Batiknet: Batik Classification-based Management Application for Inexperienced User

Muhammad Putra - Indonesia University of Education, Bandung, West Java, 40625, Indonesia
Hilmil Pradana - Sepuluh November Institute of Technology, Surabaya, East Java, 60111, Indonesia
Munawir Munawir - Indonesia University of Education, Bandung, West Java, 40625, Indonesia
Deden Pradeka - Indonesia University of Education, Bandung, West Java, 40625, Indonesia
Ana Rahma Yuniarti - Indonesia University of Education, Bandung, West Java, 40625, Indonesia
Jafar Sadik - Datokarama State Islamic University, Palu, Middle Sulawesi, 94221, Indonesia
Muhammad Andhika R - Indonesia University of Education, Bandung, West Java, 40625, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.4.3086

Abstract


Batik has significantly contributed to the Indonesian economy, is diverse, and is spread throughout cities. Currently, batik patterns are very diverse and spread from Sabang to Merauke. Each batik pattern holds distinct meanings, philosophies of life, and ancestral heritage and reflects the region where it was crafted. We introduce a new batik dataset containing five patterns: Kawung, Megamendung, Parang, Sekarjagad, and Truntum. The Convolutional Neural Network (CNN) method is an effective Deep Learning method for extracting image information. CNNs have become the state of the art for various image processing tasks, such as classification, segmentation, and object recognition. This study used several state-of-the-art architectures, including Xception, ResNet50V2, MobileNetV2, and DenseNet169. However, we chose EfficientNetV2 as the primary feature extractor due to its superior performance. Our results show that EfficientNetV2 outperformed other architectures in training, validation, and testing accuracy, making it the best choice for classifying batik patterns. The training process resulted in an accuracy of 98% for training, 97% for validation, and 96% for testing. To ensure the accessibility and practical application of this research, we developed a user-friendly, web-based interface with a RESTful API, making the tool accessible to a broader audience. The application is named "BatikNet," a name chosen to reflect the blend of traditional batik culture ("Batik") with neural network technology ("Net"). This research contributes a valuable dataset and a practical tool for future studies and applications in batik pattern recognition and supports the preservation and understanding of Indonesian cultural heritage

Keywords


Batik; Deep Learning; Convolutional Neural Network; EfficientNet; REST

Full Text:

PDF

References


Saddhono, K., Widodo, S. T., Al-Makmun, M. T., & Tozu, M.. The study of philosophical meaning of Batik and Kimono patterns to foster collaborative creative industry. Asian Social Science, 10(9), 51–61. doi: 10.5539/ass.v10n9p52

F. A. Putra et al., "Classification of Batik Authenticity Using Convolutional Neural Network Algorithm with Transfer Learning Method," 2021 Sixth International Conference on Informatics and Computing (ICIC), Jakarta, Indonesia, 2021, pp. 1-6, doi: 10.1109/ICIC54025.2021.9632937.

Rangkuti, A. H., Harjoko, A., & Putra, A. (2021). A Novel Reliable Approach for Image Batik Classification That Invariant with Scale and Rotation Using MU2ECS-LBP Algorithm. Procedia Computer Science, 179(2019), 863–870. doi:10.1016/j.procs.2021.01.075

Sarker I. H. (2021). Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN computer science, 2(6), 420. doi:10.1007/s42979-021-00815-1

Sarker, I.H. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN COMPUT. SCI. 2, 420 (2021). doi: 10.1007/s42979-021-00815-1

M. T. Dwi Putra et al., "Mini Prototype of the Futuristic Bin with an Automatic Waste Sortation System for Managing the Garbage Problems in Society," 2024 10th International Conference on Wireless and Telematics (ICWT), Batam, Indonesia, 2024, pp. 1-6, doi: 10.1109/ICWT62080.2024.10674711.

A. K. Ali, A. M. Abdullah, and S. F. Raheem, “Impact the Classes’ Number on the Convolutional Neural Networks Performance for Image Classification”, Int. J. of Adv. Sci. Comp. and Eng., vol. 6, no. 2, pp. 64–69, Jul. 2024. doi : 10.62527/ijasce.6.2.204

L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021. doi: 10.1186/s40537-021-00444-8.

I. M. A. Agastya and A. Setyanto, “Classification of Indonesian batik using deep learning techniques and data augmentation,” Proc. - 2018 3rd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. ICITISEE 2018, pp. 27–31, 2018, doi: 10.1109/ICITISEE.2018.8720990.

M. A. Rasyidi and T. Bariyah, “Batik pattern recognition using convolutional neural network,” Bull. Electr. Eng. Informatics, vol. 9, no. 4, pp. 1430–1437, 2020, doi: 10.11591/eei.v9i4.2385.

D. A. Mardani, Pranowo, and A. J. Santoso, “Deep learning for recognition of Javanese batik patterns,” AIP Conf. Proc., vol. 2217, no. April, 2020, doi: 10.1063/5.0000686.

D. G. T. Meranggi, N. Yudistira, and Y. A. Sari, “Batik Classification Using Convolutional Neural Network with Data Improvements,” Int. J. Informatics Vis., vol. 6, no. 1, pp. 6–11, 2022, doi: 10.30630/joiv.6.1.716.

D. M. S. Arsa and A. A. N. H. Susila, “VGG16 in Batik Classification based on Random Forest,” Proc. 2019 Int. Conf. Inf. Manag. Technol. ICIMTech 2019, vol. 1, no. August, pp. 295–299, 2019, doi: 10.1109/ICIMTech.2019.8843844.

C. Uswatun Khasanah, E. Utami and S. Raharjo, "Implementation of Data Augmentation Using Convolutional Neural Network for Batik Classification," 2020 8th International Conference on Cyber and IT Service Management (CITSM), Pangkal, Indonesia, 2020, pp. 1-5, doi: 10.1109/CITSM50537.2020.9268890.

Rasyidi, M. A., Handayani, R., & Aziz, F. (2021). “Identification of batik making method from images using convolutional neural network with limited amount of data”. Bulletin of Electrical Engineering and Informatics,10(3), 1300–1307. https://doi.org/10.11591/eei.v10i3.3035

S. Aras, A. Setyanto and Rismayani, "Classification of Papuan Batik Motifs Using Deep Learning and Data Augmentation," 2022 4th International Conference on Cybernetics and Intelligent System (ICORIS), Prapat, Indonesia, 2022, pp. 1-5, doi: 10.1109/ICORIS56080.2022.10031320.

Tristanto, J., Hendryli, J., & Herwindiati, D. E. (2018). Classification of Batik Motifs Convolutional Neural Networks. The 2018 International Conference on Information Technology, Engineering, Science, and Its Applications, 1–5. doi: 10.2139/ssrn.3258935

W. Bismi, D. Riana, and A. S. Hewiz, “Disease Identification on Fig Leaf Images Using Deep Learning Method”, Int. J. of Adv. Sci. Comp. and Eng., vol. 6, no. 2, pp. 57–63, Jul. 2024. doi: 10.62527/ijasce.6.2.203

A. Saxena, “An Introduction to Convolutional Neural Networks,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 10, no. 12, pp. 943–947, 2022, doi: 10.22214/ijraset.2022.47789.

A. Ajit, K. Acharya and A. Samanta, "A Review of Convolutional Neural Networks," 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India, 2020, pp. 1-5, doi: 10.1109/ic-ETITE47903.2020.049.

Singh, K., Singh, D., & Mishra, N. (2022). Review: Convolutional neural networks and its architecture. International Journal of Health Sciences, 6(S1), 9183–9190. doi:10.53730/ijhs.v6nS1.7074

Zhao, X., Wang, L., Zhang, Y. et al. A review of convolutional neural networks in computer vision. Artif Intell Rev 57, 99 (2024). doi: 10.1007/s10462-024-10721-6.

Susilawati. I, Supatman, Witanti. A, Klasifikasi Citra Virus SARS-COV Menggunakan Deep Learning. Jurnal Informatika: Jurnal Pengembangan IT, 8(2), 65–70. doi:10.30591/jpit.v8i2.4587

X. Wu, R. Liu, H. Yang and Z. Chen, "An Xception Based Convolutional Neural Network for Scene Image Classification with Transfer Learning," 2020 2nd International Conference on Information Technology and Computer Application (ITCA), Guangzhou, China, 2020, pp. 262-267, doi: 10.1109/ITCA52113.2020.00063.

Shafiq M, Gu Z. Deep Residual Learning for Image Recognition: A Survey. Applied Sciences. 2022; 12(18):8972. doi: 10.3390/app12188972

K. Dong, C. Zhou, Y. Ruan and Y. Li, "MobileNetV2 Model for Image Classification," 2020 2nd International Conference on Information Technology and Computer Application (ITCA), Guangzhou, China, 2020, pp. 476-480, doi: 10.1109/ITCA52113.2020.00106.

Gaihua Wang et al, “Study on Image Classification Algorithm Based on Improved DenseNet,” 2021 J. Phys.: Conf. Ser. 1952 022011, doi:10.1088/1742-6596/1952/2/022011

M. Iman, H. R. Arabnia, and K. Rasheed, “A Review of Deep Transfer Learning and Recent Advancements,” Technologies, vol. 11, no. 2, pp. 1–14, 2023, doi: 10.3390/technologies11020040.

M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” 36th Int. Conf. Mach. Learn. ICML 2019, vol. 2019-June, pp. 10691–10700, 2019. doi: 10.48550/arXiv.1905.11946

M. Tan and Q. V. Le, “EfficientNetV2: Smaller Models and Faster Training,” Proc. Mach. Learn. Res., vol. 139, pp. 10096–10106, 2021. doi: 10.48550/arXiv.2104.00298

Pacal, I., Celik, O., Bayram, B. et al. Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04532-1

J. Görtler et al., Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels, vol. 1, no. 1. Association for Computing Machinery, 2022. doi: 10.1145/3491102.3501823.

Ajay Kulkarni, Deri Chong, Feras A. Batarseh, 5 - Foundations of data imbalance and solutions for a data democracy, Editor(s): Feras A. Batarseh, Ruixin Yang, Data Democracy, Academic Press, 2020, Pages 83-106, ISBN 9780128183663, doi:10.1016/B978-0-12-818366-3.00005-8.

Koç, H., Erdoğan, A. M., Barjakly, Y., & Peker, S. (2021). UML Diagrams in Software Engineering Research: A Systematic Literature Review. 13. doi: 10.3390/proceedings2021074013

F. Halili and E. Ramadani, “Web Services: A Comparison of Soap and Rest Services,” Mod. Appl. Sci., vol. 12, no. 3, p. 175, 2018, doi: 10.5539/mas.v12n3p175.

D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–15, 2015. doi:10.48550/arXiv.1412.6980