Optimisasi pengambilan keputusan dalam manajemen sumber daya manusia proyek konstruksi melalui pendekatan bayesian networks - Arif Aryadhana Sugawa; Steenie E. Wallah; Arthur H. Thambas (Program Studi Teknik Sipil, Fakultas Teknik dan Perencanaan, Universitas Warmadewa, 2024)
This research focuses on the importance of human resources management (HR) in government construction projects, which often face challenges such as delays, cost overruns, and quality problems. The aim of this research is to identify the main factors that influence workforce performance in construction projects using a Bayesian network approach. In this context, factors such as workplace conditions, relations between workers, technology, materials and tools, environment, and project management and coordination have been identified as key factors that have the potential to influence project success. This research methodology involves collecting data through surveys and interviews with construction professionals. The data obtained were analyzed using Genie and SPSS V.26 software. The Bayesian network method is used to model and analyze probabilistic relationships between factors that influence workforce performance. This approach allows for more prescriptive and informed decision-making, which is critical to overcoming the challenges faced in construction projects. The analysis results show that technology and project management have a significant impact on workforce performance. Optimization carried out using the Bayesian Networks approach can reduce potential problems by up to 7-9% for the various factors analyzed. This confirms the effectiveness of this approach in increasing labor efficiency and productivity in construction projects. This research also highlights the importance of continuous improvement strategies in project management. As a result of this research, the framework developed can assist project managers in identifying and addressing critical factors that influence HR performance. Additionally, this research proposes that further implementations of Bayesian networks can be applied to other aspects of civil engineering, such as risk management and project scheduling, to improve overall project outcomes.