<![CDATA[Batik is an Indonesian intangible cultural heritage with high artistic value. However, the complexity of classical Yogyakarta patterns, particularly Parang and Kawung, characterized by intricate structures, color variations, and indistinct boundaries, poses significant challenges for automated image processing. Therefore, image segmentation becomes a crucial step in batik identification and digitalization. This study aims to develop an efficient segmentation model for Yogyakarta batik patterns using a modified SegNet architecture. The dataset comprises 720 RGB images, consisting of 360 Parang pattern images and 360 Kawung pattern images. All images were processed into binary ground truth masks through a combination of K-Means Clustering and morphological operations. The SegNet architecture was modified into three encoder and decoder blocks, employing Conv2DTranspose for upsampling and a sigmoid activation function in the output layer. The model was trained for 50 epochs using the Adam optimizer and binary cross entropy loss function. Based on evaluation on the test dataset, the modified SegNet model achieved strong performance with an accuracy of 91.72%, a mean Intersection over Union of 77.23%, and a mean Dice Coefficient of 87.07%. Visual inspection of the prediction results further confirms the model’s ability to accurately separate pattern regions from the background. These findings demonstrate that the modified SegNet architecture performs well in segmenting Parang and Kawung batik patterns and shows strong potential for supporting future batik recognition and digitalization systems.]]>
