<![CDATA[Shadow puppetry (wayang kulit) is one of Indonesia's cultural heritages with significant historical and artistic value. The complexity of digital image backgrounds in wayang kulit poses a major challenge in automatic segmentation, particularly due to lighting variations, intricate carving (tatahan) details, and the limitations of conventional methods in handling high visual variability. This study aims to implement a U-Net architecture based on Convolutional Neural Network (CNN) for segmenting images of Pandawa shadow puppet characters encompassing five main characters: Puntadewa, Janaka, Werkudara, Nakula, and Sadewa. The dataset consists of 1,500 independently collected shadow puppet images with ground truth masks divided into 1,093 training, 157 validation, and 250 test data. The U-Net model was trained using the Adam optimizer with an initial learning rate of 1×10⁻⁴, combined Binary Cross-Entropy and Dice Loss function, and 128×128 pixel input size. Early stopping and automatic learning rate adjustment via ReduceLROnPlateau were applied to optimize training and prevent overfitting throughout the learning process. The model achieved Accuracy 95.8%, AUC 98.6%, Dice Coefficient 91.9%, IoU 86.9%, Precision 91.5%, and Recall 95.0% on 250 test data. Previous studies on wayang kulit have been limited to image classification, while U-Net applications have been predominantly found in medical and satellite domains, making this study a novel contribution that addresses an existing research gap and supports the digitalization of Indonesian cultural heritage. The contribution of this study is to provide the first deep learning-based image segmentation model specifically designed to automatically separate Pandawa wayang kulit silhouettes from their backgrounds, demonstrating the effectiveness of U-Net architecture on cultural heritage objects with high visual complexity, and establishing a segmentation performance baseline for the Indonesian visual cultural heritage domain that can serve as a reference for future wayang kulit digitalization system development.]]>
