Modelling Ergonomic Hazard Risks in Manual Handling: Insights from Ponorogo’s Traditional Industry - Arifah, Dian Afif; Rahma, Ratih Andhika Akbar; Harmini, Triana; Hafidz, Dhiya Irsyad (The Indonesian Journal of Occupational Safety and Health, 2025)
Introduction: As the center-cultured region in Indonesia, Ponorogo Regency is dominated by traditional manufacturing industries which support regional economic growth. Most production in this sector is labor-intensive and depends on manual handling processes, which may increase the risk of work-related musculoskeletal disorders (WMSDs). This study aims to develop a model to evaluate and predict ergonomic hazards using a neural network algorithm, focusing on the relationship between manual handling postures and musculoskeletal pain in 12 body regions. Method: A cross-sectional study involved data of 250 workers measured using used Nordic Musculoskeletal questionnaire and manual handling exposure checklist based on SNI 9011:2021. A neural network model was developed based on GLM’s output to explore the complex interrelationships between manual handling postures (X variables) and musculoskeletal pain across 12 body regions (Y variables). Result: The outputs identified carrying object over 9 meters (X10), one-handed lifting (X3), and trunk twisting (X2), with X10 confirmed as the most predictor for multiple outcomes, affecting six regions. Neural network models demonstrated adequate learning capacity with stable architecture, proved by average CEE values ranging from 0.21 to 0.54. The models showed improved predictive accuracy across epochs. Conclusion: The finding shows that NN modelling may be expanded to include broader industries in Indonesia's traditional manufacturing sector as an integrated data-based information system application. However, further validation using external datasets is recommended to enhance generalizability.