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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Institut Teknologi Dirgantara Adisutjipto
2024-12-01
|
| Series: | Compiler |
| Subjects: | |
| Online Access: | https://ejournals.itda.ac.id/index.php/compiler/article/view/2649 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832557735081672704 |
|---|---|
| author | Muhammad Faqih Dzulqarnain Abdul Fadlil Imam Riadi |
| author_facet | Muhammad Faqih Dzulqarnain Abdul Fadlil Imam Riadi |
| author_sort | Muhammad Faqih Dzulqarnain |
| collection | DOAJ |
| description | 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 learn a lower-dimensional latent representation that captures the most salient features of the batik patterns. The performance of this enhanced model was compared against a standard convolutional neural network (CNN) without the autoencoder. Experimental results demonstrate that the incorporation of the autoencoder significantly improved the classification accuracy, achieving 99% accuracy on the testing data and loss value of 3.4%. This study highlights the potential of deep convolutional autoencoders as a powerful tool for augmenting image data and improving the performance of deep learning models in the context of batik image classification. |
| format | Article |
| id | doaj-art-f8ffef983e2e4386a4cc4599532c76d5 |
| institution | Kabale University |
| issn | 2252-3839 2549-2403 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Institut Teknologi Dirgantara Adisutjipto |
| record_format | Article |
| series | Compiler |
| spelling | doaj-art-f8ffef983e2e4386a4cc4599532c76d52025-02-03T03:08:17ZengInstitut Teknologi Dirgantara AdisutjiptoCompiler2252-38392549-24032024-12-0113212313010.28989/compiler.v13i2.2649973Improving the Accuracy of Batik Classification using Deep Convolutional Auto EncoderMuhammad Faqih Dzulqarnain0Abdul Fadlil1Imam Riadi2Universitas Ahmad Dahlan, Politeknik Aisyiyah PontianakUniversitas Ahmad DahlanUniversitas Ahmad DahlanThis 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 learn a lower-dimensional latent representation that captures the most salient features of the batik patterns. The performance of this enhanced model was compared against a standard convolutional neural network (CNN) without the autoencoder. Experimental results demonstrate that the incorporation of the autoencoder significantly improved the classification accuracy, achieving 99% accuracy on the testing data and loss value of 3.4%. This study highlights the potential of deep convolutional autoencoders as a powerful tool for augmenting image data and improving the performance of deep learning models in the context of batik image classification.https://ejournals.itda.ac.id/index.php/compiler/article/view/2649autoencoder, dcae, accuracy, batik classification |
| spellingShingle | Muhammad Faqih Dzulqarnain Abdul Fadlil Imam Riadi Improving the Accuracy of Batik Classification using Deep Convolutional Auto Encoder Compiler autoencoder, dcae, accuracy, batik classification |
| title | Improving the Accuracy of Batik Classification using Deep Convolutional Auto Encoder |
| title_full | Improving the Accuracy of Batik Classification using Deep Convolutional Auto Encoder |
| title_fullStr | Improving the Accuracy of Batik Classification using Deep Convolutional Auto Encoder |
| title_full_unstemmed | Improving the Accuracy of Batik Classification using Deep Convolutional Auto Encoder |
| title_short | Improving the Accuracy of Batik Classification using Deep Convolutional Auto Encoder |
| title_sort | improving the accuracy of batik classification using deep convolutional auto encoder |
| topic | autoencoder, dcae, accuracy, batik classification |
| url | https://ejournals.itda.ac.id/index.php/compiler/article/view/2649 |
| work_keys_str_mv | AT muhammadfaqihdzulqarnain improvingtheaccuracyofbatikclassificationusingdeepconvolutionalautoencoder AT abdulfadlil improvingtheaccuracyofbatikclassificationusingdeepconvolutionalautoencoder AT imamriadi improvingtheaccuracyofbatikclassificationusingdeepconvolutionalautoencoder |