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All Journal Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING Unram Journal of Community Service (UJCS) Jurnal Rekayasa Sistem Informasi dan Teknologi Journal of Computer Science and Information Technology Journal of Data Analytics, Information, and Computer Science (JDAICS) Jurnal Computer and Technology
Akbar, Ardiyallah
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Implementation of Conditional WGAN-GP, ResNet50V2, and HDBSCAN for Generating and Recommending Traditional Lombok Songket Motifs Akbar, Ardiyallah; Karim, Muh Nasirudin; Imran, Bahtiar
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10894

Abstract

Songket is a traditional Indonesian woven textile with profound cultural and aesthetic value, particularly in Lombok, where artisans continue to preserve its distinctive motifs. However, the creation of new designs is still carried out manually, requiring considerable time and relying heavily on the artisans’ creativity. This study proposes an integrated system that combines Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP), ResNet50V2, and HDBSCAN to automatically generate and recommend Lombok’s traditional songket motifs. The dataset consists of primary data collected directly from local artisans and secondary data from the BatikNitik public repository, thereby providing authentic yet diverse motif samples for training. CWGAN-GP is employed to synthesize motifs with stable and realistic structures across multiple epochs. Subsequently, ResNet50V2 is utilized for deep visual feature extraction, HDBSCAN for density-based clustering, and UMAP for two-dimensional visualization of motif distribution. The system successfully groups motifs into meaningful clusters, with the largest cluster containing consistent patterns of high aesthetic value. A recommendation mechanism is also developed to suggest up to five similar motifs from the original dataset within the same cluster, ensuring cultural relevance while fostering design innovation. Despite these promising outcomes, several limitations remain, such as the relatively small number of songket motif samples, variations in motif quality, and challenges during data collection including inconsistent lighting and non-uniform patterns. These factors affect both dataset consistency and generative performance. Nevertheless, this approach demonstrates the potential of artificial intelligence to support the preservation and innovation of cultural heritage by assisting artisans in creating and exploring new motifs more efficiently without losing their traditional identity.
Co-Authors ahmad yani Amiruddin Kalbuadi Buchtami, Lalu Reza Hambali Hambali Hamim, Lutfi Hamzan Wadi Imran, Bahtiar Jaelani, Lalu Rizki Juniyardi, Lalu Karim, Muh Nasirudin Kembang, Lale Puspita Lalu Darmawan Bakti, Lalu Darmawan Lalu Delsi Samsumar, M.Eng. Rudi Muslim Sriasih, Sriasih Subki, Ahmad Supardianto Supardianto Suryadi, Emi Tony Gunawan Zaeniah Zaeniah Zaenudin Zaenudin Zaenudin Zulpan Hadi