Compliance of Scaffolder Workers in Using Full Body Harness through Rewards and Punishment as Intervening Variable
Abstract
The electronics manufacturing industry demands high levels of precision and accuracy, putting workers at risk of mental workload, which can impact work effectiveness and health. This study aims to analyze the level of mental workload among technicians in the electronics assembly maintenance division and identify the dominant factors influencing it. The study used a quantitative approach using the NASA-TLX instrument on 25 technicians, along with data uniformity and adequacy analysis to ensure the validity of the results. The results showed that technicians experienced high mental workload, with the highest score being 85.60 and the lowest being 68.50. The three main dimensions that contributed most were Mental Demand (average 312), Effort (278), and Physical Demand (250). The uniformity test yielded a BKA score of 90.12 and a BKB score of 66.30, indicating that the data met uniformity and adequacy requirements. These findings have practical implications for company management in designing ergonomic strategies and work policies to reduce mental workload. Recommendations include rotating technicians to reduce concentrated cognitive load, restructuring work hours and rest periods to optimize physical recovery, and scheduling maintenance shifts to reduce perceived performance pressure. This research contributes to scientific research by providing empirical evidence on mental workload in the electronics manufacturing industry and offering applicable work management strategies to improve technician well-being and productivity.
References
Aluko, O. I. S. A. (2023). Work Related Stress Management and the Performance of Workers in Public Health Facilities in Kwara State, Nigeria (Doctoral dissertation, Kwara State University (Nigeria)).
Asyidikiah, M. R., & Herwanto, D. (2022). Analysis of mental workload of engineering division management using National Aeronautical and Space Administration (NASA)-TLX. Jurnal Serambi Engineering, 7(2), 2983–2990. https://doi.org/10.32672/jse.v7i2.393
Azemil, N., & Wahyuni, H. C. (2017). [Judul artikel tidak lengkap]. Aeronautics and Space Journal, 55(1), 81–88.
Cahyadi, A. S., & Andesta, D. (2022). Analysis of canopy product quality control at the Purnama Karya welding workshop. Jurnal Serambi Engineering, 7(1), 2672–2682. https://doi.org/10.32672/jse.v7i1.3830
Chenani, K. T., & Madadizadeh, F. (2020). A Short Review of Subjective Measures in Mental Workload Assessment. International Journal of Occupational Hygiene, 12(3), 271-273.
Chenarboo, F. J., Hekmatshoar, R., & Fallahi, M. (2022). The influence of physical and mental workload on the safe behavior of employees in the automobile industry. Heliyon, 8(10). https://doi.org/10.1016/j.heliyon.2022.e11034
DiDomenico, A., & Nussbaum, M. A. (2011). Effects of different physical workload parameters on mental workload and performance. International Journal of Industrial Ergonomics, 41(3), 255-260. https://doi.org/10.1016/j.ergon.2011.01.008
Gaillard, A. W. (1993). Comparing the concepts of mental load and stress. Ergonomics, 36(9), 991-1005. https://doi.org/10.1080/00140139308967972
Imbara, S. F., Badriah, D. L., Iswarawanti, D. N., & Mamlukah, M. (2023). Faktor-faktor yang berhubungan dengan kelelahan kerja pada operator dump truck mining dept saat shift malam di PT. X Cirebon 2023. Journal of Health Research Science, 3(2), 154–166.
Jex, H. R. (1988). Measuring mental workload: Problems, progress, and promises. In Advances in psychology (Vol. 52, pp. 5-39). North-Holland. https://doi.org/10.1016/S0166-4115%2808%2962381-X
Lestari, P. F., Muis, M., Thamrin, Y., Naiem, F., Saleh, L. M., & Arifin, M. A. (2024). Impact of Work Climate, Workload, and Stress on Fatigue for Improving Health and Work Outcomes. Integrative Biomedical Research, 8(10), 1-6. https://doi.org/10.25163/angiotherapy.8109972
Longo, L., Wickens, C. D., Hancock, G., & Hancock, P. A. (2022). Human mental workload: A survey and a novel inclusive definition. Frontiers in psychology, 13, 883321. https://doi.org/10.3389/fpsyg.2022.883321
Masri, G., Al-Shargie, F., Tariq, U., Almughairbi, F., Babiloni, F., & Al-Nashash, H. (2023). Mental stress assessment in the workplace: a review. IEEE Transactions on Affective Computing, 15(3), 958-976. https://doi.org/10.1109/TAFFC.2023.3312762
Mouzé-Amady, M., Raufaste, E., Prade, H., & Meyer, J. P. (2013). Fuzzy-TLX: using fuzzy integrals for evaluating human mental workload with NASA-Task Load indeX in laboratory and field studies. Ergonomics, 56(5), 752-763. https://doi.org/10.1080/00140139.2013.776702
Nagy, A., Spyridis, Y., Mills, G. J., & Argyriou, V. (2024). User experience evaluation of AR assisted industrial maintenance and support applications. arXiv preprint.
Nicoletti, L., & Padovano, A. (2019). Human factors in occupational health and safety 4.0: a cross-sectional correlation study of workload, stress and outcomes of an industrial emergency response. International Journal of Simulation and Process Modelling, 14(2), 178-195. https://doi.org/10.1504/IJSPM.2019.10021441
Pramesti, A., & Suhendar, E. (2021). Workload analysis using the NASA-TLX method at CV. Bahagia Jaya Alsindo. STRING (Technology Research and Innovation Writing Unit), 5(3), 229. https://doi.org/10.30998/string.v5i3.6528
Putri, N., Sari, L., & Achiraeniwati, E. (2022). Workload in the packaging box production section: Results and discussion. Journal of Production Management, 9, 9–15.
Pütz, S., Rick, V., Mertens, A., & Nitsch, V. (2022). Using IoT devices for sensor-based monitoring of employees’ mental workload: Investigating managers’ expectations and concerns. Applied ergonomics, 102(103739), 103739. https://doi.org/10.1016/j.apergo.2022.103739
Rožman, M., Oreški, D., & Tominc, P. (2023). Artificial-intelligence-supported reduction of employees’ workload to increase the company’s performance in today’s VUCA Environment. Sustainability, 15(6), 5019. https://doi.org/10.3390/su15065019
Rubio, S., Díaz, E., Martín, J., & Puente, J. M. (2004). Evaluation of subjective mental workload: A comparison of SWAT, NASA‐TLX, and workload profile methods. Applied psychology, 53(1), 61-86. https://psycnet.apa.org/doi/10.1111/j.1464-0597.2004.00161.x
Said, S., et al. (2020). Validation of the Raw National Aeronautics and Space Administration Task Load Index (NASA-TLX) questionnaire as a reliable tool for measuring subjective workload. Journal of Medical Internet Research, 22(9), e19472. https://doi.org/10.2196/19472
Sari, R. I. P., Setiowati, R., & Oktaviani, A. (2022). Mental workload analysis using NASA-TLX method on customer service employees in Strategist Informa Social Media Division (PT Home Center Kawan Lama). Nucleus, 3(1), 20–26.
Sholikhah, M., & Abdulrahim, M. (2022). Optimizing labor in the order completion production process (Case study: Konveksi Star Nine Group). Scientific Journal of Industrial Engineering.
Sönmez, B., Oğuz, Z., Kutlu, L., & Yıldırım, A. (2017). Determination of nurses' mental workloads using subjective methods. Journal of Clinical Nursing, 26(3-4), 514-523. https://doi.org/10.1111/jocn.13476
Sukma, S. I., Muis, M., & Ibrahim, E. (2019). The Influence of Noise and Hot Work Climate on Fatigue through Work Pulse on Workers of Production Division at PT. Maruki International Indonesia Makassar in 2019. East African Sch. J. Educ. Humanit. Lit, 2(11), 672-677.
Wirani, A. P., Julyanto, O., Kartini, D. A., & Mukhlasin. (2022). The effect of work shift on mental workload of maintenance operators using NASA Task Load Index (TLX). JIEMAR, 3(3). https://doi.org/10.7777/jiemar.v3i3.363
Wu, Y., Zhang, Y., & Zheng, B. (2024). Workload assessment of operators: Correlation between NASA-TLX and pupillary responses. Applied Sciences, 14(24), 11975. https://doi.org/10.3390/app142411975
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