Culturally Adaptive AI System for Wayang Character Visualization and Recognition for Children
English
Abstract
This study developed an artificial intelligence–based drawing and classification system to support the visual reinterpretation of wayang characters for children while preserving their cultural identity. The research addressed the declining interest of younger generations in traditional cultural heritage by introducing a visually engaging and culturally adaptive digital approach. A generative model based on StyleGAN-3 was trained to produce child-friendly visual adaptations of ten wayang characters, while a ResNet-18 classification model was implemented to recognize character images. The dataset consisted of 400 training images and 60 testing images, including children’s drawings used to evaluate model generalization. Image preprocessing and data augmentation techniques were applied to improve model robustness. The classification model achieved an overall accuracy of 87%, indicating strong capability in recognizing distinctive visual characteristics of wayang characters across varied visual styles. In addition, a visual preference evaluation involving children showed that several generated characters received positive responses, particularly those with balanced proportions and expressive features. The results demonstrated that the proposed system can function as an interactive cultural learning medium and provide an innovative strategy for introducing traditional wayang characters to digital-native children.
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