Harnessing the Power of Generative Adversarial Networks (GANs) for Novel Batik Designs: An Exploration of Lightweight GANs (LGANs) for Automatic Batik Design

Abstract

This study proposes the use of Generative Adversarial Networks (GANs), specifically Lightweight GANs (LGANs), as a novel approach to revitalize the batik industry in Malaysia and Indonesia, which is currently experiencing a decline in interest among young artists. By automating the generation of innovative batik designs, this technology aims to bridge the gap between traditional craftsmanship and modern innovation, offering a significant opportunity for economic upliftment and skill development for the economically underprivileged B40 community. LGANs are chosen for their efficiency in training and their capability to produce high-quality outputs, making them particularly suited for creating intricate batik patterns. The research evaluates LGANs' effectiveness in generating novel batik designs, comparing the results with those of traditional manual methods. Findings suggest that LGANs are not only capable of producing distinctive and complex designs but also do so with greater efficiency and accuracy, demonstrating the potential of this technology to attract young artists and provide sustainable income opportunities for the B40 community. This study highlights the synergy between artificial intelligence and traditional artistry as a promising direction for revitalizing the batik industry, expanding its global reach, and preserving cultural heritage while fostering innovation and inclusivity.