Javanese Batik Image Classification using Self-Organizing Map

Adhi Dharma Wibawa*, Eko Arif Wicaksono, Siti Dwi Suryani, Rumadi Rumadi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Batik has been officially recognized as one of Indonesia's cultural heritages by UNESCO, and Indonesia now has a batik day which is always celebrated every 2nd October since 2009. The diversity of Indonesian batik patterns makes it difficult to recognize. So, Batik becomes an object of research related to pattern recognition and classification. This study proposes a method for classifying batik motifs using a Self-organizing Map (SOM) on an Artificial Neural Network (ANN). This study aims to classify Javanese batik motifs through computation using artificial intelligence. The samples of the batik motifs used in this study were the Kawung, Megamendung, and Parang motifs. The amount of data used is 150 images, where the number of each motif is 50 images. In pre-processing, we convert all images to grayscale and then perform segmentation to anticipate images that are not suitable. Feature extraction is done through three algorithms, namely Gray Level Co-occurrence Matrix (GLCM), RGB (red, green, blue), and HSV (Hue, Saturation, Value). The features obtained will be divided into training and testing data. SOM is used as a classifier. The highest accuracy of 77% is obtained by using the HSV feature. When combining all the features for classification by using the same portion of data for training, we obtained an accuracy of 90%. This result showed the potential of the SOM algorithm when classifying a large number of batik patterns.

Original languageEnglish
Title of host publicationICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering
Subtitle of host publicationDigital Transformation Strategy in Facing the VUCA and TUNA Era
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages472-477
Number of pages6
ISBN (Electronic)9798350320954
DOIs
Publication statusPublished - 2023
Event2023 International Conference on Computer Science, Information Technology and Engineering, ICCoSITE 2023 - Jakarta, Indonesia
Duration: 16 Feb 2023 → …

Publication series

NameICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era

Conference

Conference2023 International Conference on Computer Science, Information Technology and Engineering, ICCoSITE 2023
Country/TerritoryIndonesia
CityJakarta
Period16/02/23 → …

Keywords

  • Batik Classification
  • GLCM
  • HSV
  • Javanese Batik Images
  • RGB
  • SOM

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