<![CDATA[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.]]>
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