Abstract

Bali’s traditional Pendet dance represents an important cultural heritage that requires preservation. To support dance recognition, this study applied semantic segmentation to Pendet dance images using the Multires U-Net architecture with alpha hyperparameter tuning. Specifically, three optimization methods Particle Swarm Optimization (PSO), Grid Search, and Random Search were evaluated using the Jaccard Index, Dice Coefficient, and Mean Squared Error (MSE). The results demonstrate that Grid Search produced the optimal alpha value of 1.45, achieving average Jaccard and Dice scores of 98.5002 and 99.2439, respectively. These results outperform previous research (98.4746; 99.2309), PSO (98.4883; 99.2378), and Random Search (98.4837; 99.2352). For MSE, the prior study reported the best score of 7.608E-04, followed by Grid Search (7.659E-04), PSO (7.663E-04), and Random Search (7.765E-04). These findings highlight the effectiveness of Grid Search in optimizing the alpha hyperparameter for the Multires U-Net architecture and demonstrate a significant performance improvement compared to earlier studies.

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