Generative Adversarial Network Implementation for Batik Motif Synthesis
| Main Authors: | Abdurrahman, Miqdad, Shabrina, Nabila Husna, Halim, Dareen K |
|---|---|
| Format: | Proceeding PeerReviewed Book Thesis |
| Bahasa: | eng |
| Terbitan: |
, 2019
|
| Subjects: | |
| Online Access: |
http://kc.umn.ac.id/12623/1/Peer%20Review%20Prosiding%20Generative%20Adversarial%20Network.pdf http://kc.umn.ac.id/12623/ https://ieeexplore.ieee.org/document/8981834/authors#authors |
| ctrlnum |
12623 |
|---|---|
| fullrecord |
<?xml version="1.0"?>
<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><relation>http://kc.umn.ac.id/12623/</relation><title>Generative Adversarial Network Implementation for Batik Motif Synthesis</title><creator>Abdurrahman, Miqdad</creator><creator>Shabrina, Nabila Husna</creator><creator>Halim, Dareen K</creator><subject>AS2.5-4 International associations, congresses, conferences, etc.</subject><description>Artificial intelligence is widely used due to its flexibility. Artificial intelligence can be used to generate and recognize patterns, for example batik motif. This study aims to generate a batik motif by utilizing a framework model made by Ian Goodfellow, namely Generative Adversarial Network (GAN) with reference to Deep Convolutional GAN (DCGAN) by Alec Radford. The training was implemented using two optimizer, RMSProp and Adam optimizer. The result shows that the networks were able to generate some pattern like batik motif and a non-batik motif pattern using RMSProp optimizer. The generated patterns were affected by the number and motifs of the dataset.</description><date>2019</date><type>Journal:Proceeding</type><type>PeerReview:PeerReviewed</type><type>Book:Book</type><language>eng</language><rights>cc_by_nc_nd_4</rights><identifier>http://kc.umn.ac.id/12623/1/Peer%20Review%20Prosiding%20Generative%20Adversarial%20Network.pdf</identifier><identifier> Abdurrahman, Miqdad and Shabrina, Nabila Husna and Halim, Dareen K (2019) Generative Adversarial Network Implementation for Batik Motif Synthesis. In: 2019 5th International Conference on New Media Studies (CONMEDIA), 9-11 Oct. 2019, Bali, Indonesia. </identifier><relation>https://ieeexplore.ieee.org/document/8981834/authors#authors</relation><recordID>12623</recordID></dc>
|
| language |
eng |
| format |
Journal:Proceeding Journal PeerReview:PeerReviewed PeerReview Book:Book Book Thesis:Thesis Thesis |
| author |
Abdurrahman, Miqdad Shabrina, Nabila Husna Halim, Dareen K |
| title |
Generative Adversarial Network Implementation for Batik Motif Synthesis |
| publishDate |
2019 |
| topic |
AS2.5-4 International associations congresses conferences etc |
| url |
http://kc.umn.ac.id/12623/1/Peer%20Review%20Prosiding%20Generative%20Adversarial%20Network.pdf http://kc.umn.ac.id/12623/ https://ieeexplore.ieee.org/document/8981834/authors#authors |
| contents |
Artificial intelligence is widely used due to its flexibility. Artificial intelligence can be used to generate and recognize patterns, for example batik motif. This study aims to generate a batik motif by utilizing a framework model made by Ian Goodfellow, namely Generative Adversarial Network (GAN) with reference to Deep Convolutional GAN (DCGAN) by Alec Radford. The training was implemented using two optimizer, RMSProp and Adam optimizer. The result shows that the networks were able to generate some pattern like batik motif and a non-batik motif pattern using RMSProp optimizer. The generated patterns were affected by the number and motifs of the dataset. |
| id |
IOS6965.12623 |
| institution |
Universitas Multimedia Nusantara |
| institution_id |
355 |
| institution_type |
library:university library |
| library |
Perpustakaan Universitas Multimedia Nusantara |
| library_id |
306 |
| collection |
Knowledge Center UMN |
| repository_id |
6965 |
| subject_area |
Business/Bisnis Communication/Komunikasi Art Apreciation/Apresiasi Seni Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| city |
TANGERANG |
| province |
BANTEN |
| shared_to_ipusnas_str |
1 |
| repoId |
IOS6965 |
| first_indexed |
2020-06-17T12:57:37Z |
| last_indexed |
2020-09-27T09:27:40Z |
| recordtype |
dc |
| _version_ |
1686477457169842176 |
| score |
17.538404 |