AI System Design for Modern Batik Pattern Generation Based on Progressive Growth of Generative Adversarial Networks (PGGAN) with the Integration of Jember Local Wisdom

Authors

  • Restu Imam Safii Politeknik Negeri Jember Author
  • Denny Trias Utomo Politeknik Negeri Jember Author https://orcid.org/0000-0001-9472-7750
  • Nitin Kumar Chandigarh Group of Colleges Author
  • Sulistiyanto Universitas Nurul Jadid Author

DOI:

https://doi.org/10.63935/akiratech.v3i3.292

Keywords:

Progressive Growing of GANs (PGGAN), Next.js, MySQL, Perancangan Sistem, Kearifan Lokal

Abstract

Batik is an Indonesian cultural heritage that has deep aesthetic and philosophical value. In Jember Regency, the Labako batik motif is a unique visual identity that represents agrarian culture through the symbol of tobacco leaves. Although Deep Learning  technologies such as Deep Convolutional Generative Adversarial Networks (DCGAN) have been used for motif generation, the model has limitations in producing high-resolution images with complex details consistently. This research aims to design an artificial intelligence system based on the Progressive Growing of Generative Adversarial Networks (PGGAN) to produce modern batik patterns that integrate Jember's local wisdom. The system development method used is the Spiral Model which includes the planning, risk analysis, development, and evaluation stages. This system is designed using the Next.js  framework for the user interface and MySQL as a database management system to store assets and order information. The result of this research is in the form of a system architecture design and workflow that is able to progressively increase image resolution starting from $16 \times 16$ pixels to $2000 \times 2000$ pixels. This design is expected to be a technical foundation in the development of digital platforms that are able to preserve local culture through generative technological innovation.

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References

[1] I. R. Salma, A. A. Wibowo, and Y. Satria, "Coffee and Cocoa in the Creation of Typical Batik Motifs of Jember," Din. Crafts and Batik Maj. Ilm., vol. 32, no. 2, p. 63, Jun. 2015, doi: 10.22322/dkb.v32i2.1362.

[2] A. S. Pribadi, N. W. W. Sari, S. Palupi, and R. Zulkarnain, “Enhancing Computational Strategies to Decode ChatGPT’s Influence on the Critical Thinking Abilities of University Students”.

[3] O. Octadion, N. Yudistira, and D. Kurnianingtyas, “Synthesis of Batik Motifs using a Diffusion -- Generative Adversarial Network,” Jul. 22, 2023, arXivarXiv:2307.12122. doi:10.48550/arXiv.2307.12122.

[4] T. Widodo, S. I. Ishak, T. Haryato, and A. B. Santoso, "Exploration of New Batik Patterns with Deep Convolutional Algorithm Generative Adversarial Networks (DCGANs)," Inform. Mulawarman J. Ilm. Computing Science., vol. 18, no. 1, p. 40, Feb. 2023, doi: 10.30872/jim.v18i1.9531.

[5] D. Mahardika, "MAKING A MATHEMATICAL BATIK PATTERN DESIGN USING THE FORMULA OF IMPLICIT TRIGONOMETRY FUNCTIONS IN THE CARTESIAN FIELD," Sebatik, vol. 27, no. 1, pp. 223–230, Jun. 2023, doi: 10.46984/sebatik.v27i1.2219.

[6] H. Kapoh, O. E. Melo, and A. A. Kimbal, “Black Box Testing in Web-based Applications: Case Study - Remedial Application at Manado State Polytechnic,” Int. J. Comput. Appl., vol. 174, no. 12, pp. 21–24, Jan. 2021, doi: 10.5120/ijca2021921002.

[7] A. Beers et al., “High-resolution medical image synthesis using progressively grown generative adversarial networks,” May 09, 2018, arXivarXiv:1805.03144. doi:10.48550/arXiv.1805.03144.

[8] C.-I. Kim, M. Kim, S. Jung, and E. Hwang, “Simplified Fréchet Distance for Generative Adversarial Nets,” Sensors, vol. 20, no. 6, p. 1548, Mar. 2020, doi: 10.3390/s20061548.

[9] Sulistiyanto, S., Ahmad, T., Suromo, I., Jailani, A., & Fiqri, A. (2025). Training on Making Emergency Electrical Emergency Boxes for Rapid Disaster Response with Solar Power Plants in Bucor Wetan Village, Pakuniran District, Probolinggo Regency. Gotong Royong, 2(3), 189-196. https://doi.org/10.63935/gr.v2i3.212

[10] Fitrony, F. A., & Elim, M. I. (2026). Implementation of Smart Farming Technology for Monitoring Water Availability in Lettuce Plants Based on the Internet of Things. Journal of Science and Engineering, 2(1), 7-16.

[11] Prayogi, A. T., & Gunasti, A. (2026). Analysis of Investment Feasibility of the Construction of the Jakarta-Cikampek Elevated Toll Road (MBZ) Using NPV, IRR, BCR, and Payback Period Methods. Journal of Science and Engineering, 2(1), 22-29.

[12] Sulistiyanto, S., Widoretno, S., & Enggar, R. (2025). Training on Making Hybird Energy Water Pump with Wind and Solar Power for Agricultural Irrigation in Banyuglugur Village. Gotong Royong, 2(2), 93-99. https://doi.org/10.63935/gr.v2i2.149

[13] Gunawan, G., Palupi, S., & Hardi, R. (2024). Eksploitasi Aplikasi Web Commerce Sebagai Sarana Pendukung Penjualan Produk Umkm Di Balikpapan Timur. Gotong Royong, 1(3), 134-140.

[14] Pahrizal, M. Y., Idris, N. B., & Insan, P. P. (2026). Integrasi Generative AI dan Large Language Model pada Aplikasi Web untuk Transformasi Sketsa Menjadi Gambar Realistis dan Evaluasi Otomatis. Journal of Information Technology, Software Engineering and Computer Science (ITSECS), 4(4), 283-299.

[15] Satria, D. A., Pirmansah, I. A., & Musthofa, R. A. (2025). Implementasi generatif artificial intelligence pada tahap pembuatan animasi menggunakan metode MDLC. Jurnal Teknologi Informasi dan Multimedia, 7(4), 663-674.

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Published

2026-11-05

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Articles

How to Cite

AI System Design for Modern Batik Pattern Generation Based on Progressive Growth of Generative Adversarial Networks (PGGAN) with the Integration of Jember Local Wisdom. (2026). Akiratech, 3(3), 127-134. https://doi.org/10.63935/akiratech.v3i3.292

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