Optimization of Contract Change Management and Team Integration in the Critical Center Building Construction Project and Support Services at Dr. Moewardi Regional General Hospital
DOI:
https://doi.org/10.32832/astonjadro.v15i3.21637Keywords:
team integration, construction project performance, K-Means Clustering, contract change management, multiple linear regressionAbstract
The increase in the number of patients and the complexity of medical cases at Dr. Moewardi Regional General Hospital has prompted the construction of a Critical Center and Support Services Building as modern facilities for handling critical conditions. However, this type A government hospital project requires effective management of contract changes and coordination between teams across stakeholders. Contract changes in government projects are strictly limited, which has implications for cost efficiency and timeliness. This study was conducted to evaluate the effect of contract change management on time and cost performance, assess the effectiveness of team integration, and formulate concepts for optimizing both. This study was conducted through a quantitative survey of 39 respondents, including project owners, planning consultants, supervisory consultants, and contractors. The survey focused on contract change management indicators and team integration indicators, and was supplemented by project document analysis and in-depth interviews with relevant parties. The results showed a strong influence on overall contract change management (R² = 0.962). K-Means clustering identified three patterns of team integration: positive in cooperation (T5 = 3.21) and responsibility (T8 = 3.31), neutral in mutual trust (T6/T7 = 3.13), and negative in formal communication (T1 = 2.51) and regular meetings (T3 = 2.74). The novelty of this study lies in its integrative approach, which combines regression and clustering simultaneously to analyze and optimize contract management and team integration, with recommendations for strengthening formal communication, BIM, cloud-based platforms, and periodic team evaluations that can be replicated in large-scale health infrastructure projects.
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