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All Journal JIEET (Journal of Information Engineering and Educational Technology) Teknika
Abdilqoyyim, Fanky
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Ancient Javanese Manuscript Reconstruction Using Generative Adversarial Network with StarGAN v2 Variations Wibowo, Kukuh Cokro; Damayanti, Fitri; Abdilqoyyim, Fanky
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1182

Abstract

Ancient Javanese manuscripts are part of Indonesia's cultural heritage; most of them are usually in bad condition due to the age and environmental surroundings. This paper presents a manuscript reconstruction using the Generative Adversarial Network model, using the variation of StarGAN v2. The primary objective of this research is to assist philologists in reconstructing damaged manuscripts more efficiently, reducing the time and effort compared to manual reconstruction methods. The training for 100 epochs is performed by the model in order to generate the reconstruction image closest to ground truth. This study is done on a dataset that consists of a set of damaged manuscript images. In this dataset, 80% is for training, 20% is for validation, and 10 images are used for testing. Quality assessment will be made on image outputs during training, based on PSNR, SSIM, and LPIPS metrics. The results indicate that the PSNR increases from 16.1234 dB at the 50th epoch to 17.5588 dB at the 100th epoch, while the SSIM increases from 0.8374 to 0.8519, showing a strong improvement in image quality. Despite the LPIPS having a very slight increase from 0.1020 to 0.1051, this evidences that the model can be further improved. Overall, this study demonstrates that the StarGAN v2 model is effective in reconstructing ancient Javanese manuscripts-a great contribution to the field of cultural heritage preservation using modern technology.
Batik Sketch Coloring Using Generative Adversarial Network Pix2pix Abdilqoyyim, Fanky; Muhammad Ali Syakur; Fitri Damayanti
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n2.p113-128

Abstract

Batik, an Indonesian cultural heritage recognized by UNESCO, involves a complex and time-consuming coloring process. Digitalization offers a crucial solution for the preservation and development of batik art in the modern era. This research implements a Generative Adversarial Network (GAN), specifically the Pix2Pix model, to automate the transformation of batik sketches into colored images. The primary focus is a performance comparison between the U-Net generator architecture, which excels at preserving spatial details via skip-connections, and the ResNet architecture, which is capable of learning deeper and more complex features. This study utilized 1164 paired images, divided into 931 training, 117 validation, and 116 test data points. The models were trained with consistent hyperparameters, including an Adam optimizer and a combination of L1 and binary cross-entropy loss functions, with evaluations at 50 and 100 epochs. Quantitative evaluation was performed using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Fréchet Inception Distance (FID) metrics. The results indicate that the model with the ResNet generator trained for 100 epochs achieved the most balanced and superior performance, with a PSNR of 8.11, SSIM of 0.39, and an FID of 120.72. Overall, the ResNet generator proved more capable of producing realistic and high-quality colored batik images, offering an innovative solution to enhance the efficiency of the coloring process while supporting cultural preservation.
Co-Authors Fitri Damayanti Fitri Damayanti Syakur, Muhammad Ali Wibowo, Kukuh Cokro