Modified MobileNet-V2 Convolution Neural Network (CNN) for Character Identification of Surakarta Shadow Puppets

Shadow puppets or in Indonesian called as “wayang kulit” is one of Indonesia's native traditional arts that still exists to this day. This art form has been recognised by UNESCO since 2003. Wayang kulit is not just ordinary entertainment. It carries profound moral values, but is gradually being forgotten by the younger generation. To facilitate the public in recognizing wayang kulit characters, a desktop-based application was developed using Canny edge detection for image extraction and a modified MobileNet-V2 CNN algorithm for character identification. The dataset used in this research was sourced from Google and Instagram, with 22 names of wayang kulit characters serving as classes. The identification results for 1,312 wayang kulit images (test data) using the classic CNN model yielded an accuracy of 50%, precision of 53%, and recall of 47%. Meanwhile, with the modified MobileNet-V2 CNN model, called custom CNN gives an accuracy of 92%, precision of 93%, and recall of 92%. From the result, it is shown that the custom CNN has high performance, where it has a few false positive predictions in detecting the characters of wayang kulit. Additionally, the result shows that the CNN model is robust and reliable for the task of identifying the wayang kulit characters. Based on the result, the model can be applied in preserving and promoting traditional wayang kulit art by helping to catalog and identify characters, making it more accessible to a wider audience, including the younger generation.

@article{Solichin2025,
  title     = {Modified MobileNet-V2 Convolution Neural Network (CNN) for Character Identification of Surakarta Shadow Puppets},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Achmad Solichin and Dwi Pebrianti and Painem and Sanding Riyanto},
  doi       = {10.14569/IJACSA.2025.0160534},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160534}
}