TY - GEN
T1 - Javanese Batik Image Classification using Self-Organizing Map
AU - Wibawa, Adhi Dharma
AU - Arif Wicaksono, Eko
AU - Suryani, Siti Dwi
AU - Rumadi, Rumadi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Batik Classification
KW - GLCM
KW - HSV
KW - Javanese Batik Images
KW - RGB
KW - SOM
UR - http://www.scopus.com/inward/record.url?scp=85163127561&partnerID=8YFLogxK
U2 - 10.1109/ICCoSITE57641.2023.10127783
DO - 10.1109/ICCoSITE57641.2023.10127783
M3 - Conference contribution
AN - SCOPUS:85163127561
T3 - ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era
SP - 472
EP - 477
BT - ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Computer Science, Information Technology and Engineering, ICCoSITE 2023
Y2 - 16 February 2023
ER -