Enhanced Semarang Batik Classification using MobileNetV2 and Data Augmentation

Authors

  • Emila Khoirunnisa Universitas Dian Nuswantoro
  • Farrikh Alzami Universitas Dian Nuswantoro
  • Ricardus Anggi Pramunendar Universitas Dian Nuswantoro
  • Rama Aria Megantara Universitas Dian Nuswantoro
  • Muhammad Naufal Universitas Dian Nuswantoro
  • Harun Al-Azies Universitas Dian Nuswantoro
  • Sri Winarno Universitas Dian Nuswantoro

DOI:

10.33395/sinkron.v9i1.14308

Keywords:

Batik Pattern Recognition, MobileNetV2, Deep Learning Classification, Data Augmentation, Cultural Heritage Preservation, Computer Vision

Abstract

Batik, an Indonesian cultural heritage recognized by UNESCO, faces challenges in pattern identification and documentation, particularly for the younger generation. Previous studies on batik classification have shown limitations in handling small datasets and maintaining accuracy with limited computational resources. This research proposes an enhanced classification approach for Semarang Batik motifs using MobileNetV2 architecture combined with strategic data augmentation techniques. The study utilizes a dataset of 3,020 images comprising 10 distinct Semarang Batik motifs, implementing horizontal flipping, rotation, and zoom transformations to address dataset limitations. Our methodology incorporates transfer learning through ImageNet pre-trained weights and custom layer modifications to optimize the MobileNetV2 architecture for batik-specific features. The model achieves 100% accuracy on validation data, with precision, recall, and F1-scores consistently above 0.98 across all classes. The confusion matrix analysis reveals minimal misclassification between similar motif patterns, particularly in the Batik Blekok Warak and Batik Kembang Sepatu classes. This research contributes to cultural heritage preservation by providing an efficient, resource-conscious solution for automated batik pattern recognition, potentially supporting educational and commercial applications in the batik industry.

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How to Cite

Khoirunnisa, E., Alzami, F., Pramunendar, R. A. ., Megantara, R. A. ., Naufal, M. ., Al-Azies, H. ., & Winarno, S. . (2025). Enhanced Semarang Batik Classification using MobileNetV2 and Data Augmentation. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(1), 43-54. https://doi.org/10.33395/sinkron.v9i1.14308

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