IJBTT

Productivity Model of Labour on Construction Projects in Indonesia

Productivity Model of Labour on Construction Projects in Indonesia
	© 2022 by IJETT Journal
	Volume-70 Issue-11
	Year of Publication : 2022
	Authors : Novisca M. Anditiaman, Rusdi Usman Latif, Irwan Ridwan Rahim, Rosmariani Arifuddin
	DOI : 10.14445/22315381/IJETT-V70I11P223

How to Cite?

Novisca M. Anditiaman, Rusdi Usman Latif, Irwan Ridwan Rahim, Rosmariani Arifuddin, "Productivity Model of Labour on Construction Projects in Indonesia," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 211-218, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I11P223

Abstract
Productivity and construction are two concepts that are connected. Construction is a labor-intensive sector of the economy, and employers value employees as a resource. This study aims to assess labor productivity on Indonesian construction projects, examine the variables that affect labor productivity on Indonesian construction projects, and create a model for predicting labor productivity on Indonesian construction projects. A questionnaire survey and information on road preservation projects obtained from the Indonesian Ministry of PUPR's Directorate General of Highways for the 2018–2022 fiscal year comprise the quantitative methodology employed in this study. Ratio Output/Input, Partial Least Square (smartPLS), Autoregressive Integrated Moving Average (ARIMA), and Structural Equation Modeling (SEM) were employed in the data analysis. The findings indicated that from 2018 to 2022, labor productivity decreased. As measured in kilometers per day per person, Region I's labor productivity was 2.4652, 2.2094, 1.7079, 1.8826, and 1.8879. In region II, the corresponding labor productivity values were 3.1724, 2.3126, 1.9292, 2.2208, and 2.2045 (km/day/person) and 1.7141, 1.9103, 1.6525, 1.8632, and 1.6302 (km/day/person) were the respective labor productivity figures for Region III. Internal labor, field circumstances, time, and finances significantly affect labor productivity in Regions I, II, and III. The projection results for 2023 to 2027 were obtained based on the outcomes of the processing of the three labor productivity statistics in each region (I, II, and III). 1.8157, 1.8017, 1.8295, 1.8400, and 1,8301 (km/day/person) where the corresponding values for areas I. The corresponding values for region II were 2.0287, 2.2569, 2.2689, 2.2602 and 2.2477 (km/day/person). The corresponding values for area III were 1.7980, 1.6771, 1.7641, 1.7015, and 1.7466 (km/day/person). Therefore, the output amount of the job and the length and number of contracted labor inputs make up the conceptual model of labor productivity on construction projects in Indonesia that has been used in this study.

Keywords
Productivity, Labor, Construction project, SmartPLS, ARIMA.

Reference
[1] Construction Industry Productivity Analysis Review.
[2] Ahn S, Lee S and Steel R. P, “Construction Workers' Perceptions and Attitudes toward Social Norms as Predictors of Their Absence Behavior,” Journal of Construction Engineering and Management, vol. 140, no. 5, pp. 04013069, 2014. Crossref, https://doi.org/10.1061/(asce)co.1943-7862.0000826
[3] Saad Saleem Khan, Mohsin Amjad, Imtiaz Hussain Gilani, Stephen Larkin, Hussain Shareef and Waqar Ali Sher, "High Voltage & Short Circuit Testing Laboratories in Economic Growth of African Region: A Prospect for Prosperity," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 67-78, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P209
[4] Bantar J, Lukman M and Rachman R, “Labor Productivity in the Construction Project of the East Seram Regency Financial Office,” Paulus Civil Engineering Research, vol. 1, no. 2, 2021
[5] Christopher T. Mgonja, "Enhancing the University-Industry Collaboration in Developing Countries through Best Practices," International Journal of Engineering Trends and Technology (IJETT), vol. 50, no. 4, pp. 216-225, 2017. Crossref, https://doi.org/10.14445/22315381/IJETT-V50P235
[6] Chileshe N, Civil T, Science U, & Civil T, “Providing Project Work Environment Effects on Labor Productivity: Perception of Road Iran Parviz Ghoddousi Omid Poorafsyar Abstract,” EPPM, pp. 87–97, 2013.
[7] Dadi G. B, Goodrum P. M, Taylor T. R. B and Carswell C. M, “Cognitive Workload Demands Using 2D and 3D Spatial Engineering Information Formats,” Journal of Construction Engineering and Management, vol. 140, no. 5, pp. 04014001, 2014. Crossref, https://doi.org/10.1061/(asce)co.1943-7862.0000827
[8] Mohit Taneja and Arpan Manchanda, "Six Sigma an Approach to Improve Productivity in Manufacturing Industry," International Journal of Engineering Trends and Technology, vol. 5, no. 6, pp. 281-286, 2013
[9] Djatnika S. S, Supandji B. S, Abidin I. S and Trigunarsyah B, “Improving Construction Workforce Performance by Restructuring the Framework for Classification, Qualifications and Work Competency Standards,” Seminar on the 25th Anniversary of Construction Engineering and Management Education in Indonesia, pp. 1–10, 2005. Crossref, https://doi.org/10.13140/2.1.3767.9366
[10] Gurmu A. T, “Tools for Measuring Construction Materials Management Practices and Predicting Labor Productivity in Multistory Building Projects,” Journal of Construction Engineering and Management, vol. 145, no. 2, pp. 04018139, 2018. Crossref, https://doi.org/10.1061/(asce)co.1943-7862.0001611.
[11] Heravi G and Eslamdoost E, “Applying Artificial Neural Networks for Measuring and Predicting Construction-Labor Productivity,” Journal of Construction Engineering and Management, vol. 141, no. 10, pp. 04015032, 2015. Crossref, https://doi.org/10.1061/(asce)co.1943-7862.0001006
[12] Jovana Jovanovic, "Buildings' Site Management and Supervision: Cost Analysis of Labor Positions," International Journal of Engineering Trends and Technology vol. 69, no. 5, pp. 242-248, 2021. Crossref, https:// doi.org/10.14445/22315381/IJETT-V69I5P231
[13] Hutasoit J. P and Sibi et al., “Productivity Analysis of Construction Workers on Ceramic Floor Pairs and Wall Plastering Using Work Sampling Method,” Journal of Civil Statistics, vol. 5, no. 4, pp. 205–214, 2017
[14] Jarkas A. M., “Analysis and Measurement of Buildability Factors Influencing Rebar Installation Labor Productivity of In Situ Reinforced Concrete Walls,” Journal of Architectural Engineering, vol. 18, no. 1, pp. 52–60, 2012. Crossref, https://doi.org/10.1061/(asce)ae.1943-5568.0000043
[15] Jarkas A. M, “Effect of Buildability on Labor Productivity: A Practical Quantification Approach,” Journal of Construction Engineering and Management, vol. 142, no. 2, pp. 06015002, 2016. Crossref, https://doi.org/10.1061/(asce)co.1943-7862.0001062
[16] Jr., R. D. E., Asce M, Lee S and Asce M, “Measuring Project Level Productivity on Transportation Projects,” American Society of Civil Engineers, vol. 132, no. 3, pp. 314–320, 2006. Crossref, https://doi.org/10.1061/(ASCE)0733-9364(2006)132
[17] Leônidas Alves de Souza, Tiago Bittencourt Nazaré and Patrícia Werneck Silva de Oliveira, "The Productivity Mapping, Classification and Analysis of a Mining Company Electrical Maintenance Team," International Journal of Engineering Trends and Technology, vol. 63, no. 2, pp. 66-71, 2018. Crossref, https://doi.org/10.14445/22315381/IJETT-V63P211
[18] Nurhadi A, Labor Utility Rate Comparison of the Productivity of Construction Workers in Regular Working Hours and Overtime in the Construction of High-rise Buildings in Surabaya Agus, vol. 1, pp. 27–32, 2015.
[19] Ludfi Djakfar, Wisnumurti and Lila Khamelda, "Methods of Making Laboratory Scale CPHMA Specimens," International Journal of Engineering Trends and Technology, pp. 6-9, 2020. Crossref, https://doi.org/10.14445/22315381/IJETT-AIIC102
[20] Seyis S, “Pros and Cons of Using Building Information Modeling in the AEC Industry,” Risks of Building Information Mode, vol. 145, no. 8, pp. 1–15, 2019. Crossref, https://doi.org/10.1061/(ASCE)CO.1943
[21] Neelima Suresh and Sahimol Eldhose, "A Review on Factors Affecting Productivity of Multi-Storey Building Construction," SSRG International Journal of Civil Engineering, vol. 5, no. 1, pp. 5-9, 2018. Crossref, https://doi.org/10.14445/23488352/IJCE-V5I2P102
[22] Energi P, Focus L, Hanafi M. H, Zhen O. M, Razak A. A, and Pinang P, Infrastructure Project, vol. 1, no. 1, pp. 68–78, 2021.
[23] Vereen S. C, Rasdorf W and Hummer J. E, “Development and Comparative Analysis of Construction Industry Labor Productivity Metrics,” Journal of Construction Engineering and Management, vol. 142, no. 7, pp. 04016020, 2016. Crossref, https://doi.org/10.1061/(asce)co.1943-7862.0001112
[24] Wu W, Hartless J, Tesei A, Gunji V, Ayer S and London J, “Design Assessment in Virtual and Mixed Reality Environments: Comparison of Novices and Experts,” Journal of Construction Engineering and Management, vol. 145, no. 9, pp. 04019049, 2019. Crossref, https://doi.org/10.1061/(asce)co.1943-7862.0001683
[25] Prakash H. Panda and Mr. Sahajanand Kamat, "Productivity Analysis of Pile Driving Equipment in Mumbai," SSRG International Journal of Civil Engineering, vol. 4, no. 5, pp. 57-65, 2017. Crossref, https://doi.org/10.14445/23488352/IJCE-V4I5P122
[26] Sheikh N. A, Ullah F, Ayub B and Thaheem M. J, “Labor Productivity Assessment Using Activity Analysis on Semi High-Rise Building Projects in Pakistan,” Engineering Journal, vol. 21, no. 4, pp. 273–286, 2017. Crossref, https://doi.org/10.4186/ej.2017.21.4.273