Comparative Study of CNN, Vision Transformer, and Hybrid CNN–ViT Models for Indonesian Batik Pattern Classification
Abstract
Batik is an Indonesian cultural heritage with unique visual characteristics and deep philosophical value. The complexity of motifs, color variations, and geometric details make batik classification an interesting challenge in the field of computer vision. This study conducted a comparative study between three deep learning approaches for classifying Indonesian batik motifs using Convolutional Neural Network (CNN), Vision Transformer (ViT), and a hybrid CNN–ViT model. The dataset used includes more than 3,000 batik images from various regions in Indonesia, with a variety of motifs such as Yogyakarta Kawung, Aceh, Ceplok, and Megamendung.Each model was trained with uniform parameters and augmentations to ensure fair evaluation, resulting in CNN accuracy of 94.43% F1-macro 93.45%, ViT accuracy of 91.55% F1-macro 89.78%, and Hybrid CNN-ViT accuracy of 94.04% F1-macro 92.91%. This is reinforced by the combination of modules (EfficientNet-B2 + CBAM + ArcFace) that can improve model performance furthermore, This study contributes to the development of an automated batik classification system and supports cultural preservation through artificial intelligence- based digitization.
References
E. Sugiarto, F. Budiman, and A. Fahmi, “Implementation of Deep Learning Based on Convolution Neural Network for Batik Pattern Recognition,” Kinetik: Game Technology, Infor- mation System, Computer Network, Computing, Electronics, and Control, vol. 10, no. 1, pp. 11–16, Feb. 2025, doi: 10.22219/kinetik.v10i1.2019.
I. P. Sari and L. Elvitaria, “Data-driven Approach for Batik Pattern Classification Using Convolutional Neural Network (CNN),” Jurnal Mandiri, vol. 13, no. 3, pp. 323–331, 2025.
D. Trisnawarman, A. A. Supriyanton, V. C. Mawardi, and U. A. Okengwu, “Comparative Study of CNN and Vision Transformers on Indonesian Traditional Cakes Classification,” International Journal of Advances in Artificial Intelligence and Machine Learning, vol. 2, no. 2, pp. 86–94, July 2025, doi: 10.58723/ijaaiml.v2i2.405.
N. Puspitasari, A. Tejawati, and A. P. A. Masa, “East Kali- mantan Batik Image Classification Using CNN Architecture with Advanced Feature Extraction,” International Journal on Electrical Engineering and Informatics, vol. 17, no. 2, pp. 223– 237, June 2025, doi: 10.15676/ijeei.2025.17.2.6.
N. Setyawan, C.-C. Sun, M.-H. Hsu, W.-K. Kuo, and J.-W. Hsieh, “FaceLiVT: Face Recognition Using Linear Vision Transformer with Structural Reparameterization for Mobile Device,” in Proc. IEEE Int. Conf. Image Processing (ICIP), 2025, doi: 10.1109/ICIP55913.2025.11084611.
P.-S. Xie, J.-L. Wang, H. Wang, Y.-C. Pan, X.-Y. Li, and T. Feng, “Industrial Internet Vulnerability Detection Method Based on CBAM-CNN-SVM,” International Journal of Net- work Security, vol. 25, no. 3, pp. 385–393, May 2023, doi: 10.6633/IJNS.202305.
M. Dahbali, N. Aboutabit, and N. Lamghari, “A Hybrid Model for Arabic Script Recognition Based on CNN-CBAM and BLSTM,” Jordanian Journal of Computers and Information Technology (JJCIT), vol. 10, no. 3, pp. 294–302, Sept. 2024.
M. F. Dzulqarnain, A. Fadlil, and I. Riadi, “Performance Comparison of Learned Features from Autoencoder and Shape- Based Hu Moments for Batik Classification,” Jurnal Teknik Informatika (JUTIF), vol. 6, no. 4, pp. 1729–1744, Aug. 2025, doi: 10.52436/1.jutif.2025.6.4.4827.
M. B. Kurniawan and E. Utami, “Comparative Analysis of Contrast Enhancement Methods for Classification of Pekalon- gan Batik Motifs Using Convolutional Neural Network,” Jurnal Teknik Informatika (JUTIF), vol. 5, no. 6, pp. 1779–1787, Dec. 2024, doi: 10.52436/1.jutif.2024.5.6.2621.
D. A. Ramadhan and D. Ramadhani, “Classification of Riau Batik Motifs Using the Convolutional Neural Network (CNN) Algorithm,” International Journal of Electrical, Energy and Power System Engineering, vol. 7, no. 3, pp. 201–211, Nov. 2024, doi: 10.31258/ijeepse.7.3.201-211.
A. M. Akbar, M. Perdana, M. Fajar, and A. M. Mappalotteng, “Enhancing Batik Classification Leveraging CNN Models and Transfer Learning,” International Journal on Informatics Visu- alization, vol. 7, no. 2, pp. 354–361, 2023. [Online]. Available: https://www.joiv.org/index.php/joiv
J. Deng, J. Guo, J. Yang, N. Xue, I. Kotsia, and S. Zafeiriou, “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 9, pp. 4037–4050, 2022, doi: 10.1109/TPAMI.2021.3087709.
