The Impact of The Wage System on Estimating Construction Worker Productivity
DOI:
https://doi.org/10.15294/jtsp.v27i2.15020Keywords:
Construction workers, Neural Network, Productivity, Wage systemAbstract
A construction project's productivity significantly depends on its workforce's efficiency. This study examines the effect of different wage systems—daily wage and piece-rate (borongan) systems—on the productivity of construction workers, using a neural network model for analysis. Data were collected from workers at the ESP-Control Building Project of PLTU Units 9 & 10 through field observations and questionnaires. Productivity was assessed via the Work Sampling method, while SPSS software was employed to analyze key factors influencing worker performance. The results show that the piece-rate system is more effective in enhancing productivity than the daily wage system, as indicated by higher Labour Utilization Rates (LUR). A neural network model developed for productivity estimation achieved high accuracy, with R² values of 0.815 for the daily wage system and 0.817 for the piece-rate system. Practically, these findings can help project managers improve scheduling efficiency, minimize idle time, and reduce labor costs by adopting an appropriate wage system.
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