A Web-Based for Demak Batik Classification Using VGG16 Convolutional Neural Network

Salma Shafira Fatya Ardyani (University of Dian Nuswantoro)
Christy Atika Sari (University of Dian Nuswantoro)

Advance Sustainable Science, Engineering and Technology (ASSET)
Vol. 6 No. 4 (2024): August-October

The diversity of Demak batik motifs presents challenges in classification and identification. This research aims to develop a Demak batik motif classification system using deep learning and VGG16 convolutional network. A dataset of Demak batik images is collected and processed to train the model. The VGG16 architecture is modified by fine-tuning to optimize the classification performance. Results show that the modified VGG16 model achieved a classification accuracy of 98.72% on the test dataset, demonstrating its potential application in preserving and digitizing Demak batik cultural heritage.