A Proposed Batik Automatic Classification System Based on Ensemble Deep Learning and GLCM Feature Extraction Method

Classification of batik images is a challenge in the field of digital image processing, considering the complexity of patterns, colors, and textures of various batik motifs. This study proposes an ensemble method that combines texture feature extraction using Gray Level Co-occurrence Matrix (GLCM) with the Residual Neural Network (ResNet) classification model to improve accuracy in batik image classification. Texture features such as contrast, dissimilarity, entropy, homogeneity, mean, and standard deviation are extracted using GLCM and combined with ResNet to produce a more robust classification model. The experimental results show that the proposed method achieves high performance, namely above 90% for each evaluation metric used, such as accuracy, precision, recall and F-1 Score. The best performance in classifying batik images is obtained by the Standard Deviation feature with accuracy, precision, recall, and F1-score of 95%, 93%, 93%, and 93%, respectively. Furthermore, the application of the ensemble method based on the hard voting approach has proven effective in increasing the accuracy of batik image classification by utilizing a combination of texture features and deep learning models. The proposed method makes a significant contribution to the efforts to preserve batik culture through digitalization and can be implemented for various purposes such as an image-based batik search system.

@article{Elvitaria2024,
  title     = {A Proposed Batik Automatic Classification System Based on Ensemble Deep Learning and GLCM Feature Extraction Method},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Luluk Elvitaria and Ezak Fadzrin Ahmad Shaubari and Noor Azah Samsudin and Shamsul Kamal Ahmad Khalid and Salamun and Zul Indra},
  doi       = {10.14569/IJACSA.2024.0151058},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151058}
}