Vol. 2 No. 2 (2026): Vol 2 No 2 (November 2026)
Articles

Design and Development of an Acehnese Batik Motif Classification System Based on Convolutional Neural Network and Explainable AI

PDF
  • Reyhan Putra Syahmi,
  • Muhammad Khadafi,
  • Azhar Azhar,
  • Syarifah Annisa Salsabila
Reyhan Putra Syahmi
Politeknik Negeri Lhokseumawe
Muhammad Khadafi
Politeknik Negeri Lhokseumawe
Azhar Azhar
Politeknik Negeri Lhokseumawe
Syarifah Annisa Salsabila
Universitas Negeri Medan

Published 19-10-2026

Keywords

  • Batik Aceh,
  • Computer Vision,
  • Convolutional Neural Network,
  • VGG16,
  • Transfer Learning,
  • Streamlit,
  • Explainable AI
    • ...More
    Less

Copyright (c) 2026 Reyhan Putra Syahmi, Muhammad Khadafi, Azhar Azhar, Syarifah Annisa Salsabila (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Abstract

Batik Aceh is an Indonesian intangible cultural entity characterized by high visual morphological complexity and historical depth. Conventional motif identification processes face limitations due to observer subjectivity, high spectral variance, and the intricate geometric structures composing the ornaments. Therefore, this study aims to design an automated digital image classification architecture utilizing the Convolutional Neural Network (CNN) algorithm via VGG16 implementation empowered by Transfer Learning based on ImageNet pre-trained weights. To demystify the deep learning black-box nature, the computational ecosystem is integrated with Explainable AI using Gradient-weighted Class Activation Mapping (Grad-CAM) for visual inference transparency, alongside an interactive Virtual Try-On module deployed on the Streamlit web platform. The classification scope encompasses four distinct classes: Bukan Batik (Non-Batik), Pinto Aceh, Pucok Reubong, and Rencong. The dataset comprises 700 balanced images (175 per class) sourced from the Roboflow public repository and partitioned into 80% training (560 images), 10% validation (68 images), and 10% testing (72 images). The model was compiled with the Adam optimizer (learning rate 10⁻⁴), Categorical Cross-Entropy loss, and supervised by dynamic callbacks (EarlyStopping and ReduceLROnPlateau). Empirical evaluation on 72 unseen test images yielded an overall accuracy of 97.00%, an average precision of 97.50%, a recall of 97.00%, and an F1-score of 97.00%. Specifically, the Pinto Aceh and Pucok Reubong motifs achieved a perfect 100% score across all metrics, while Rencong reached a recall of 88.89%. Comprehensive Black-Box testing across 10 functional interface scenarios and White-Box code logic verification demonstrated 100% operational reliability, confirming that the proposed system is highly robust for digital cultural heritage preservation.

PDF

Downloads

Download data is not yet available.

References

  1. N. Fajira, Indrawati, dan Aswandi, 2022, “Klasifikasi Citra Batik Aceh Menggunakan Metode K-Nearest Neighbor (K-NN) Berbasis Android,” Jurnal Teknologi Rekayasa Informasi dan Komputer (JTRIK), vol. 6, no. 1, pp. 45–52, Politeknik Negeri Lhokseumawe, ISSN: 2797-1724.
  2. A. Zayyan dan D. Afriadi, 2023, “Changes and Adaptations of Acehnese Pinto Motifs in Modern Craft Arts,” in Proc. 2nd Int. Conf. on Aceh Civilization (ICAC 2023), pp. 112–120, Banda Aceh, Indonesia.
  3. A. R. Dani dan I. Handayani, 2024, “Klasifikasi Motif Batik Yogyakarta Menggunakan Metode GLCM dan CNN,” Jurnal Teknologi Terpadu, vol. 10, no. 2, pp. 142–156, doi: 10.54914/jtt.v10i2.842.
  4. D. Anastasya, B. P. Adhi, dan R. Prasetyo, 2024, “Implementasi Metode Convolutional Neural Network (CNN) Dalam Klasifikasi Motif Batik,” Nuansa Informatika, vol. 18, no. 1, pp. 78–87.
  5. W. N. Afifah dan V. Lusiana, 2025, “Klasifikasi Jenis Batik Semarangan Menggunakan Metode Convolution Neural Network (CNN),” Jurnal Ilmiah Penelitian dan Pembelajaran Informatika (JIPI), vol. 10, no. 1, pp. 542–553, doi: 10.29100/jipi.v10i1.5901.
  6. K. Azmi, S. Defit, dan Sumijan, 2023, “Implementasi Convolutional Neural Network (CNN) Untuk Klasifikasi Batik Tanah Liat Sumatera Barat,” Jurnal Unitek, vol. 16, no. 1, pp. 25–36, doi: 10.52072/unitek.v16i1.512.
  7. A. Adithama, B. Y. Dwiandiyanta, dan Wiadji, 2023, “Klasifikasi Batik Menggunakan Pre-trained VGG16 dengan Transfer Learning,” Jurnal Teknologi Informasi, vol. 7, no. 2, pp. 120–129.
  8. R. F. Alya, M. Wibowo, dan Paradise, 2023, “Classification of Batik Motif Using Transfer Learning on Convolutional Neural Network (CNN),” Jurnal Teknik Informatika (JUTIF), vol. 4, no. 1, pp. 161–170, doi: 10.20884/1.jutif.2023.4.1.728.
  9. S. S. F. Ardyani dan C. A. Sari, 2024, “A Web-Based for Demak Batik Classification Using VGG16 Convolutional Neural Network,” Applied Science, Engineering and Technology (ASSET), vol. 6, no. 4, pp. 312–324.
  10. A. Prayoga, D. R. S. Saputro, and B. Prasetiyo, 2023, “Arsitektur Convolutional Neural Network untuk Model Klasifikasi Citra Batik Yogyakarta,” Journal of Applied Computer Science and Technology (JACOST), vol. 4, no. 2, pp. 82–89, doi: 10.52158/jacost.v4i2.534.
  11. M. M. A. Wona, A. S. Adam, dan F. A. Putri, 2023, “Klasifikasi Batik Indonesia Menggunakan Convolutional Neural Network (CNN),” Jurnal Rekayasa Teknologi Informasi (JURTI), vol. 7, no. 2, pp. 185–194.
  12. I. Maulida, 2021, “Klasifikasi Kain Khas Batik dan Kain Khas Sasirangan Menggunakan Metode Convolutional Neural Network,” Skripsi, Jurusan Teknik Informatika, Universitas Muhammadiyah Banjarmasin.
  13. F. M. Shanni, A. B. Setiawan, dan M. R. A. Pratama, 2026, “Classification of Indonesian Batik Motifs using CNN VGG16 with Transfer Learning and Fine-Tuning,” Jurnal Sistem Informasi (Sistemasi), vol. 15, no. 6, pp. 2228–2243, doi: 10.32520/stmsi.v15i6.3512.
  14. S. Arifin dan Nurfaizah, 2024, “Klasifikasi Motif Batik Menggunakan Metode Convolutional Neural Network (CNN) Dengan Multi Class Classification,” Jurnal Ilmiah IT CIDA, vol. 10, no. 1, pp. 55–66.
  15. S. R. Utaminingsih dan I. Sahputra, 2024, “Pengenalan Otomatis Pola Batik Aceh Berbasis Arsitektur EfficientNet,” Jurnal Teknologi Informasi dan Komputer, vol. 10, no. 3, pp. 215–224.
  16. C. J. Anders, L. Weber, D. Neumann, W. Samek, K.-R. Müller, and S. Lapuschkin, 2026, “Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy,” PLOS ONE, vol. 21, no. 2, p. e0298412, doi: 10.1371/journal.pone.0298412.
  17. K. Muchtar, M. F. Nugraha, F. Arnia, and R. Muharar, 2025, “Edge AI-Based Detection for Defective Coffee Beans Using Deep Learning and Streamlit Framework,” IEEE Access, vol. 13, pp. 14205–14218, doi: 10.1109/ACCESS.2025.3421102.
  18. A. Bendale and T. E. Boult, 2016, “Towards Open Set Deep Networks,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1563–1572, doi: 10.1109/CVPR.2016.173.
  19. L. Brigato, B. Barz, L. Iocchi, and J. Denzler, 2022, “Image Classification with Small Datasets: Overview and Benchmark,” IEEE Access, vol. 10, pp. 121281–121301, doi: 10.1109/ACCESS.2022.3223128.
  20. [20] Murinto, S. Winiarti, dan I. Faisal, 2024, “Particle Swarm Optimization Algorithm for Hyperparameter Convolutional Neural Network and Transfer Learning VGG16 Model,” Journal of Computer Science and Information Technology (JCoSITTE), vol. 5, no. 1, pp. 474–480, doi: 10.37859/jcositte.v5i1.6841.
  21. D. M. Asriny dan R. Jayadi, 2023, “Transfer Learning VGG16 for Classification Orange Fruit Images,” Journal of Soft Computing and Mathematics Systems (JSMS), vol. 13, no. 1, pp. 206–217.
  22. H. Ardiansyah dan T. Desyani, 2025, “Transfer Learning Menggunakan Model VGG16 untuk Klasifikasi Citra Hewan,” Jurnal Pustaka AI, vol. 5, no. 2, pp. 441–448.
  23. R. S. Pressman and B. R. Maxim, 2020, Software Engineering: A Practitioner’s Approach, 9th ed., McGraw-Hill Education, New York, NY, USA, ISBN: 978-1259872976.
  24. G. J. Myers, C. Sandler, and T. Badgett, 2011, The Art of Software Testing, 3rd ed., John Wiley & Sons, Inc., Hoboken, NJ, USA, doi: 10.1002/9781119202486, ISBN: 978-1-118-03196-4.
  25. A. Escalante-Viteri and D. Mauricio, 2025, “Artificial Intelligence in Software Testing: A Systematic Review of a Decade of Evolution and Taxonomy,” Algorithms, vol. 18, no. 3, p. 112, doi: 10.3390/a18030112.