Identification of Batik in Central Java using Transfer Learning Method

Authors

  • Stephanie Pamela Adithama Universitas Atma Jaya Yogyakarta
  • B. Yudi Dwiandiyanta Universitas Atma Jaya Yogyakarta
  • Sevia Berliana Wiadji Universitas Atma Jaya Yogyakarta

DOI:

https://doi.org/10.24002/jbi.v14i02.6977

Keywords:

batik, transfer learning, Convolutional Neural Network, deep learning, VGG16

Abstract

Batik was recognized as a human heritage for oral and nonmaterial culture by UNESCO due to its symbolic and philosophical ties to the lives of Indonesians. However, the younger generation is gradually losing its legacy because of technological and sociological changes that have influenced Indonesian batik. Consequently, batik knowledge is disappearing. A convolutional neural network and transfer learning techniques were utilized in deep learning to construct a model recognising batik motifs. The study utilized a dataset of one thousand images, five classes of batik designs (Banji, Kawung, Slope, Parang, and Slobog), and pre-trained architectural models VGG16 and VGG19 on Keras. The best model utilizes the VGG16 architecture, and the number of epochs is 50, with the result of testing accuracy of 0.9200.

References

Q. Yuan, S. Xu, & L. Jian, “A new method for retrieving batik shape patterns,” Journal of the Association for Information Science and Technology, vol. 69, no. 4, pp. 578-599, 2018.

“Arti kata batik - Kamus Besar Bahasa Indonesia (KBBI) Online.” https://kbbi.web.id/batik (accessed Mar. 22, 2022).

W. Rofianto, M. G. Haque, D. Sunarsi, A. Purwanto, & H. S. H. Sapari, “Cultural Product Branding, Antecedents, And Its Implications: A Study On The Context Of Indonesian Batik,” International Journal Of Social, Policy And Law, vol. 2, no. 2, pp. 37-46, 2021.

W. Steelyana, “Batik, a beautiful cultural heritage that preserve culture and support economic development in Indonesia,” Binus Business Review, vol. 3, no. 1, pp. 116, 2012.

R. A. Rahadi, D. Rahmawati, N. A. Windasari, & P. F. Belgiawan, “The Analysis of Consumers' Preferences for Batik Products in Indonesia,” Review of Integrative Business and Economics Research, vol. 9, pp. 278-287, 2020.

C. Y. Wang, “Building a network for preserving intangible cultural heritage through education: A study of Indonesian batik,” International Journal of Art & Design Education, vol. 38, no. 2, pp. 398-415, 2019.

S. I. S. Shaharuddin, M. S. Shamsuddin, M. H. Drahman, Z. Hasan, N. A. Mohd Asri, A. A. Nordin, & N. M. Shaffiar, “A review on the Malaysian and Indonesian batik production, challenges, and innovations in the 21st century,” SAGE Open, vol. 11, no. 3, 2021.

H. Widoyo, “Melestarikan budaya batik pada generasi muda,” Feb. 02, 2021. https://binus.ac.id/character-building/2021/02/melestarikan-budaya-batik-pada-generasi-muda/(accessed Mar. 28, 2022).

V. J. Basiroen, & J. O. Oetojo. (2018, November). Digital Campaign as a Solution to Engage the Millennial’s Awareness about Javanese Batik Pesisir. In 1st International Conference (vol. 276).

A. N. Haniah, & P. F. Belgiawan, “Proposed Marketing Strategy for Increasing Batik Danar Hadi Purchase Intention among Gen Y and Z,” International Journal of Current Science Research and Review, vol. 6, no. 1, pp. 229-237, 2023.

T. Bariyah, M. A. Rasyidi, & Ngatini, “Convolutional Neural Network Untuk Metode Klasifikasi Multi-Label Pada Motif Batik,” Jurnal Teknologi Informasi Techno.Com, vol. 20, no. 1, pp. 155–165, 2021.

Pulung Adi Nugroho, I. Fenriana, & R. Arijanto, “Implementasi Deep Learning Menggunakan Convolutional Neural Network (CNN) Pada Ekspresi Manusia,” JURNAL ALGOR, vol. 2, no. 1, pp. 12–21, 2020.

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Sep. 2014, [Online]. Available: http://arxiv.org/abs/1409.1556

R. F. Alya, M. Wibowo, & P. Paradise, “Classification of Batik Motif using Transfer Learning on Convolutional Neural Network (CNN),” Jurnal Teknik Informatika (Jutif), vol. 4, no. 1, pp. 161-170, 2023.

I. M. A. Agastya & A. Setyanto, “Batik Classification Utilizing Deep Learning Techniques and Data Augmentation,” in Proceedings - 2018 3rd International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2018, Jul. 2018, pp. 27–31. doi: 10.1109/ICITISEE.2018.8720990.

S. Febrian Tumewu, D. H. Setiabudi, & I. Sugiarto, “Klasifikasi Motif Batik menggunakan metode Deep Convolutional Neural Network dengan Data Augmentation,” Jurnal Infra, vol. 8, no. 2, pp. 189–194, 2020.

R. Mawan, “Klasifikasi motif batik menggunakan Convolutional Neural Network,” JNANALOKA, vol. 1, no. 1, pp. 45–50, Apr. 2020, doi: 10.36802/jnanaloka.2020.v1-no1-45-50.

D. A. Mardani, Deep Learning untuk Pengenalan Pola Batik Jawa, Yogyakarta, Jul. 2019.

A. Kusrianto, Batik Filosofi, Motif dan Kegunaan, Penerbit Andi, 2013.

A. N. Syafitri, Tanaman Bunga Telang Dan Kawung Sebagai Inspirasi Penciptaan Busana Pesta Muslim (Doctoral dissertation, Institut Seni Indonesia Yogyakarta), 2023.

N. W. Kartika, LKP: Studi Kasus pada Yayasan Sekolah Ekspor Nasional: Perencanaan Ekspor ke Uni Emirat Arab pada UMKM Eunoia Bateek (Doctoral dissertation, Universitas Dinamika), 2023.

I. B. K. Sudiatmika & I. G. A. A. S. Dewi, “Pengenalan Karakter Wayang Bali dengan Arsitektur VGG-16,” Jurnal Teknologi Informasi dan Komputer (JUTIK), vol. 7, no. 1, pp. 47–53, 2021.

S. Tammina, “Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images,” International Journal of Scientific and Research Publications (IJSRP), vol. 9, no. 10, pp. 143–150, 2019.

C. Yulian, “Penerapan Deep Learning Style Transfer untuk Menciptakan Kombinasi Gambar Digital dengan Batik,” Universitas Multimedia Nusantara, Tangerang, 2021.

I. Chebbi, “VGG19 For VQGR: Visual Generation of Relevant Natural Language Questions from Radiology Images for Anomaly Detection,” in Conference and Labs of the Evaluation Forum, September 21–24, 2021.

Downloads

Published

2023-10-01

Issue

Vol. 14 No. 02 (2023): Jurnal Buana Informatika, Volume 14, Nomor 2, Oktober 2023

Section

Computational Science

License

Creative Commons License

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

Copyright of this journal is assigned to Jurnal Buana Informatika as the journal publisher by the knowledge of author, whilst the moral right of the publication belongs to author. Every printed and electronic publications are open access for educational purposes, research, and library. The editorial board is not responsible for copyright violation to the other than them aims mentioned before. The reproduction of any part of this journal (printed or online) will be allowed only with a written permission from Jurnal Buana Informatika.

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