Using Convolutional Neural Network and Saliency Maps for Cirebon Batik Recognition - Aditiya, Yoga; Overbeek, Marlinda Vasty; Pomalingo, Suwito (ULTIMATICS, 2025)
Cirebon Batik is one of Indonesia's cultural heritages that has its own unique patterns and motifs, reflecting the cultural richness and history of its region of origin. This study aims to address the challenges in classifying the complex motifs of Cirebon Batik by implementing Convolutional Neural Network (CNN) and Saliency Map methods. The three main motifs used are Mega Mendung, Singa Barong, and Keratonan. The dataset was obtained from various online sources and processed using image augmentation techniques. CNN is used to recognize complex visual patterns, while Saliency Map highlights important areas in the image that influence the model's decision. The results show that the developed CNN model achieved an accuracy of 82%, precision of 83%, recall of 82%, and F1-score of 82%. The use of Saliency Map provides better interpretability and enhances the understanding of the classification process