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All Journal Advance Sustainable Science, Engineering and Technology (ASSET)
Salma Shafira Fatya Ardyani
University of Dian Nuswantoro
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Journal : Advance Sustainable Science, Engineering and Technology (ASSET)

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A Web-Based for Demak Batik Classification Using VGG16 Convolutional Neural Network Salma Shafira Fatya Ardyani; Christy Atika Sari
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.771

Abstract

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.
A Web-Based for Demak Batik Classification Using VGG16 Convolutional Neural Network Salma Shafira Fatya Ardyani; Christy Atika Sari
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.771

Abstract

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.
Co-Authors Christy Atika Sari