Abstract
Indonesia is a country that has diverse natural resources, cultures, and languages. One of the cultural diversities in Indonesia is Batik, which is an Indonesian cultural heritage consisting of cloth drawn by hand using traditional techniques. To assist the public in recognizing various batik motifs, a classification method was developed to identify the types of batik through input images. The classification method uses Convolutional Neural Network (CNN) based on MobileNet V1 and ResNet-152 V2 architecture. This research uses a dataset consisting of 3300 batik images from six different batik motifs, namely ceplok, parang, nitik, megamendung, kawung, and tambal. The optimal classification model was obtained using ResNet-152 V2 architecture with shear pre-processing method and RMSprop optimizer with test accuracy value of 89.67% and validation loss of 0.44.
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References
Damayanti, N.Y., Pandanwangi, A., Sukapura, B., Dewi, A.M.A.: Innovation of Gutha Tamarin’s Batik Colet technique and visual language on Batik Bercerita ornaments by female artists. In: Damayanti, N.Y., Sayahdikumullah, D., Maulina, R. (eds.) ARTESH: 2nd International Conference on Art for Technology, Science, and Humanities, Bandung: Visual Art Study Program, Bandung Institure of Technology, pp. 67–76 (2020)
Rasyidi, M.A., Bariyah, T.: Batik pattern recognition using convolutional neural network. Bull. Electr. Eng. Inform. 9(4), 1430–1437 (2020). https://doi.org/10.11591/eei.v9i4.2385.
Qiram, I., Buhani, Rubiono, G.: Batik Banyuwangi: aesthetic and technical comparison of Coastal Batik. LEKESAN: Interdiscip. J. Asia Pac. Arts 1(2), 79–85 (2018)
Dewi Girsang, N., Muhathir: Classification of batik images using multilayer perceptron with histogram of oriented gradient feature extraction. In: Lin Liu, H., Shafiq, M., Wen Kin, H. (eds.) 4th Proceeding International Conference on Science and Engineering (ICSE), Medan: Faculty of Science and Technology Sunan Kalijaga State Islamic University Yogyakarta, pp. 197–204 (2021)
Sartana, Saptandari, E.W., Helmi, A.F.: Youth imagination about being an Indonesian. In: Hidayati, I., Kurniawan, R., Aulia, R. (eds.) Proceedings of the 1st International Conference on Psychology and Health Issues (ICOPHI 2022). Atlantis Press, Yogyakarta pp. 65–81 (2023). https://doi.org/10.2991/978-94-6463-212-5_8.
Bhatt, D., et al.: CNN variants for computer vision: history, architecture, application, challenges and future scope. Electronics 10(20), 1–28 (2021). https://doi.org/10.3390/electronics10202470.
Purwaningsih, T., Ayu Anjani, I., Bekti Utami, P.: Convolutional neural networks implementation for chili classification. In: International Symposium on Advanced Intelligent Informatics (SAIN), pp. 190–194 (2018)
Hendriyana, H., Maulana, Y.H.: Identification of types of wood using convolutional neural network with MobileNet architecture. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4(1), 70–76 (2020). https://doi.org/10.29207/resti.v4i1.1445.
Sae-Lim, W., Wettayaprasit, W., Aiyarak, P.: Convolutional Neural Networks Using MobileNet for skin lesion classification. In: 2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE) , pp. 242–247. IEEE, Songkhla (2019). https://doi.org/10.1109/JCSSE.2019.8864155.
Mohapatra, S., Abhishek, N.V.S., Bardhan, D., Ghosh, A.A., Mohanty, S.: Comparison of MobileNet and ResNet CNN architectures in the CNN-based skin cancer classifier model. Bhubaneswar (2021). https://doi.org/10.1002/9781119792611
Tseng, F.H., Yeh, K.H., Kao, F.Y., Chen, C.Y.: Mininet: dense squeeze with depthwise separable convolutions for image classification in resource-constrained autonomous systems. ISA Trans. 132, 120–130 (2023). https://doi.org/10.1016/j.isatra.2022.07.030.
Prasetyo, E., Purbaningtyas, R., Adityo, R.D., Suciati, N., Fatichah, C.: Combining MobileNetV1 and depthwise separable convolution bottleneck with expansion for classifying the freshness of Fish Eyes. Inf. Process. Agric. 9(4), 485–496 (2022). https://doi.org/10.1016/j.inpa.2022.01.002.
Koluguri, N.R., Park, T., Ginsburg, B.: TitaNet: neural model for speaker representation with 1D depth-wise separable convolutions and global context. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2022). https://doi.org/10.48550/arXiv.2110.04410.
Wang, W., Hu, Y., Zou, T., Liu, H., Wang, J., Wang, X.: A new image classification approach via improved MobileNet models with local receptive field expansion in shallow layers. Comput. Intell. Neurosci. 2020, 1–10 (2020). https://doi.org/10.1155/2020/8817849
Rachburee, N., Punlumjeak, W.: Lotus species classification using transfer learning based on VGG16, ResNet152V2, and MobileNetV2. IAES Int. J. Artif. Intell. (IJ-AI) 11(4), 1344–1352 (2022). https://doi.org/10.11591/ijai.v11.i4.pp1344-1352.
Meranggi, D.G.T., Yudistira, N., Sari, Y.A.: Batik classification using convolutional neural network with data improvements. Int. J. Inform. Vis. 6(1), 6–11 (2022). https://doi.org/10.30630/joiv.6.1.716.
Hussain, M.A.I., Khan, B., Wang, Z., Ding, S.: Woven fabric pattern recognition and classification based on deep convolutional neural networks. Electronics (Basel) 9(6), 1–12 (2020). https://doi.org/10.3390/electronics9061048.
Zhang, X., Gao, W., Pan, R.: Deep neural network with strip pooling for image classification of Yarn-Dyed plaid fabrics. CMES – Comput. Model. Eng. Sci. 130(3), 1533–1546 (2022). https://doi.org/10.32604/cmes.2022.018763
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Sani Zulkarnaen, A.C. et al. (2024). Application of Convolutional Neural Network Method with MobileNet V1 and ResNet-152 V2 Architecture in Batik Motif Classification. In: Barolli, L. (eds) Advances on Broad-Band and Wireless Computing, Communication and Applications. BWCCA 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 186. Springer, Cham. https://doi.org/10.1007/978-3-031-46784-4_6
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