Video classification of Indonesian traditional dance using a hybrid CNN-LSTM model with pose estimation

Irawan, Candra and Hadi, Heru Pramono and Jatmoko, Cahaya and Doheir, Mohamed A. S. (2025) Video classification of Indonesian traditional dance using a hybrid CNN-LSTM model with pose estimation. Bulletin of Electrical Engineering and Informatics, 15 (1). pp. 787-798. ISSN 2089-3191

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Abstract

The preservation and recognition of traditional Indonesian dances face challenges due to limited digital documentation and declining intergenerational transmission. Manual annotation of dance videos is time-consuming and prone to subjectivity, creating urgency for automated solutions. This study proposes a deep learning-based approach combining convolutional neural networks (CNN) for spatial feature extraction and long short-term memory (LSTM) for temporal modeling to recognize traditional dance movements from video sequences. The system leverages OpenPose for keypoint detection and gesture estimation, enabling frame-wise pose representation prior to classification. A hyperparameter tuning process was applied to optimize the CNN-LSTM architecture using 80% of the dataset for training and 20% for testing. Experimental results show the proposed model achieved a macro accuracy of 98.4%, with perfect precision, recall, and F1-score. This research contributes to cultural heritage digitization and intelligent video analysis by enabling accurate, real-time classification of traditional dances, providing a foundation for future systems in education, archiving and motion-driven applications.

Item Type:	Article
Uncontrolled Keywords:	Convolutional neural network, Cultural heritage, Indonesian dance recognition, Long short-term memory, Pose estimation
Divisions:	Faculty of Technology Management and Technopreneurship
Depositing User:	Sabariah Ismail
Date Deposited:	13 Jul 2026 07:42
Last Modified:	13 Jul 2026 07:42
URI:	http://eprints.utem.edu.my/id/eprint/30202
Statistic Details:	View Download Statistic

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