Improving the Classification Accuracy of Parang Batik Motifs with High Visual Similarity Through the Integration of GLCM and MobileNetV2

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Published: Apr 7, 2026

Abstract:

Background: Despite its high aesthetic value, automatic classification of Parang Surakarta batik is difficult due to the extreme textural similarities between sub-motifs. Standard CNN architectures, including MobileNetV2often fail to detect the subtle textural details that distinguish each variation of the motif.


Aims: This study develops a hybrid classification model that combines manual and automated spatial texture features to improve identification accuracy on motifs with high visual similarity.


Methods: Using a dataset that has been expanded to 120 original images (40 per class) which is then augmented to a total of 1,200 images to ensure stronger model generalization. This methodology hybrid GLCM-MobileNetV2architecture through transfer learning techniques. Features from both methods are combined through feature fusion before being classified using a Dense layer.


Result: The hybrid GLCM-MobileNetV2model achieved an accuracy of 99%. This performance outperformed the pure MobileNetV2 method (66.67%) and GLCM-SVM (85%), demonstrating that texture features provide significant discriminatory power against similar repetitive patterns.


Conclusion: The integration of GLCM and MobileNetV2 is highly effective for classifying visually similar batik motifs, achieving a superior accuracy of 99% compared to the pure MobileNetV2 (66.67%). This hybrid approach provides a robust and efficient solution for digital cultural preservation on mobile devices.

Keywords: Parang Batik , Convolutional Neural Network , Feature Fusion , GLCM , MobileNetV2

Authors:
1 . Haryanto
Asia e University,
https://orcid.org/0009-0008-7398-9186
2 . Husna Sarirah Husin
Taylor University,
https://orcid.org/0000-0001-7530-5835
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Copyright (c) 2026 Haryanto, Husna Sarirah Husin

Issue
Vol. 2 No. 1 (2026): Journal of Sustainable Software Engineering and Information Systems
Section
Articles

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