Comparative Analysis of Field Labor Productivity Coefficient with AHSP 2025 in Lightweight Brick Work (Case Study: Construction of Al Azhar University Medan Classroom Building)
Keywords:
Labor Productivity, Lightweight Brick, 2025 AHSP, Multiple Linear Regression, Productivity FactorsAbstract
Labor productivity is an important factor in the success of construction projects, affecting the cost, time, and quality of work. However, in practice, field productivity often does not conform to the standards set by the 2025 Analysis of Unit Price for Work (AHSP). This study aims to analyze labor productivity in lightweight brick masonry work, compare it with the 2025 AHSP, and identify the factors that influence labor productivity. The method used was direct field observation with the time study method over five working days, combined with a questionnaire survey distributed to the workforce. The data were analyzed using multiple linear regression to determine the effect of education, work experience, work discipline, work motivation, wages, age, and managerial factors on productivity. The results show that the average field productivity coefficients over the five days of observation were 0.1160 OH/m² for laborers, 0.2230 OH/m² for masons, 0.0306 OH/m² for head masons, and 0.0153 OH/m² for foremen. Comparison with the 2025 AHSP revealed differences in every labor category: laborers showed a difference of -5.07% (indicating higher field productivity than the standard), while masons, head masons, and foremen showed differences of 13.97%, 2.23%, and 1.25% respectively (indicating lower field productivity than the standard). The regression analysis further showed that not all variables significantly affect productivity; only wages (X5) and managerial factors (X7) had a significant effect on productivity in lightweight brick masonry work. It is concluded that a gap exists between field labor productivity and the 2025 AHSP standard, and that several factors influence labor productivity. Therefore, the use of AHSP as a reference should be adjusted to actual field conditions in order to optimize project planning and implementation.
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