Classification of Traditional Balinese Kites Using CNN for Cultural Preservation

Authors

  • Ni Wayan Sumartini Saraswati Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
  • Eddy Hartono Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
  • Ketut Jaya Atmaja Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
  • Welda Institut Bisnis dan Teknologi Indonesia
  • I Dewa Made Krishna Muku Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia

DOI:

10.33395/sinkron.v10i3.16181

Keywords:

Convolutional Neural Networks, Cross Validation, Deep Learning, Image Classification, Balinese Kites

Abstract

The digital preservation of cultural heritage has become increasingly important in sustaining local traditions amid rapid modernization. Balinese traditional kites represent a distinctive form of intangible cultural heritage with unique visual characteristics; however, their identification and classification are still largely based on subjective expertise. This research develops a Convolutional Neural Network (CNN)-based model for image classification to automatically recognize three primary types of Balinese traditional kites: Bebean, Janggan, and Pecukan. Beyond technical implementation, this research contributes to the development of a culturally specific visual dataset, addressing the limited representation of local heritage objects in mainstream computer vision research, which is predominantly based on global datasets of generic objects. A balanced dataset of 2,400 images was constructed and evaluated using 5-Fold Cross Validation to assess model stability and generalization capability. The proposed CNN model achieved an average validation accuracy of 91.5%, with balanced precision, recall, and F1-score across folds. Further evaluation on an independent test set of 282 images resulted in an accuracy of 87.94%, indicating a generalization gap of approximately 4%, which remains within an acceptable range. The results demonstrate that CNN-based classification can effectively support structured digital documentation of traditional kites. This study highlights the potential of computer vision not only as a technical tool, but also as a strategic approach to advancing data-driven cultural preservation and expanding AI applications within localized cultural contexts.

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How to Cite

Saraswati, N. W. S., Hartono, E., Ketut Jaya Atmaja, Welda, W., & Muku, I. D. M. K. (2026). Classification of Traditional Balinese Kites Using CNN for Cultural Preservation. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(3), 1381-1390. https://doi.org/10.33395/sinkron.v10i3.16181

Issue

Vol. 10 No. 3 (2026): Article Research July 2026

Section

Articles

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Copyright (c) 2026 Ni Wayan Sumartini Saraswati, Eddy Hartono, Ketut Jaya Atmaja, Welda, I Dewa Made Krishna Muku

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

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