Improving the Accuracy of Batik Classification using Deep Convolutional Auto Encoder
This research investigates the development of model deep convolutional autoencoders to enhance the classification of digital batik images. The dataset used was sourced from Kaggle. The autoencoder was employed to enrich the image data prior to convolutional processing. By forcing the autoencoder to...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institut Teknologi Dirgantara Adisutjipto
2024-12-01
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| Series: | Compiler |
| Subjects: | |
| Online Access: | https://ejournals.itda.ac.id/index.php/compiler/article/view/2649 |
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