A Web-Based for Demak Batik Classification Using VGG16 Convolutional Neural Network - Salma Shafira Fatya Ardyani; Christy Atika Sari (Science and Technology Research Centre Universitas PGRI Semarang, 2024)
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.