Ermatita, Ermatita (2022) Palembang songket fabric motif image detection with data augmentation based on ResNet using dropout. Turnitin Universitas Sriwijaya. (Submitted)
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Abstract
A good way to spread knowledge about Palembang songket woven cloth patterns is to use information technology, especially artificial intelligence technology. This study's main goal is to develop a ResNet model with dropout regularization methods and find out how dropout regularization affects the ResNet model for detecting Palembang songket fabric motif with more data. Data was collected in places like tujuh saudara songket, Zainal songket, songket PaSH, AMS songket, and batik, Ernawati songket, Nabilah collections, Ilham songket, and Marissa songket. We used eight class of data for this research. A dataset of 7,680 data for training, 960 data for validation, and 960 data for testing is a dataset that has been prepared to be implemented in experiments. In the final results, the experimental results for DResNet demonstrated that accuracy at the training stage was 92.16%, accuracy at the validation stage was 78.60%, and accuracy at the submission stage was 80.3%. The experimental results also show that dropouts are able to increase the accuracy of the ResNet model by adding +1.10% accuracy in the training process, adding +1.80% accuracy in the validation process, and adding +0.40% accuracy in the testing process.
| Item Type: | Other |
|---|---|
| Subjects: | #3 Repository of Lecturer Academic Credit Systems (TPAK) > Results of Ithenticate Plagiarism and Similarity Checker |
| Divisions: | 09-Faculty of Computer Science > 55101-Informatics (S2) |
| Depositing User: | Dr Ermatita zuhairi |
| Date Deposited: | 25 Jun 2024 06:06 |
| Last Modified: | 25 Jun 2024 06:06 |
| URI: | http://repository.unsri.ac.id/id/eprint/147723 |
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