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All Journal Jurnal Indonesia Sosial Teknologi Journal of Engineering Science and Technology Management
Pratiwi Adi, Henny
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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Risk Factor Analysis on Contingency Costs of Phase I Wastewater Network Project in Bogor City Triatmojo, Doni; Pratiwi Adi, Henny; Poedjiastoeti, Hermin
Jurnal Indonesia Sosial Teknologi Vol. 6 No. 2 (2025): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v6i2.8856

Abstract

Contingency cost estimation is essential for construction project management to anticipate cost uncertainty due to various risks. This study analyzes risk-based contingency cost estimation in the Bogor City Phase I wastewater network system project. The background of this research is based on the need for accurate cost planning to avoid budget overruns and ensure project sustainability. The study aims to identify risk factors that affect contingency costs, group risks based on their degree of influence, and build a more accurate estimation model using the Partial Least Squares-Structural Equation Modeling (PLS-SEM) approach. This study uses a mixed method, namely qualitative analysis through interviews and literature studies to identify risk factors and a quantitative approach by distributing questionnaires to project stakeholders. The data was analyzed using SPSS 25 and SmartPLS software to test the validity and reliability of the contingency cost estimation model. The study results show that the most significant risk in the pre-construction stage is licensing and design, while in the construction stage is procuring equipment/materials and force majeure factors. The highest contingency cost estimate is in the preconstruction stage, with a value of 11.02%. The study's conclusion confirms that risk mitigation in the early stages of the project is essential to reduce the impact of budget uncertainty. The implications of this study contribute to the management of construction projects by providing a more accurate risk-based contingency cost estimation model that can be used as a reference for stakeholders in planning and managing infrastructure projects.
Comparative Study of Cost Significant Model and Artificial Neural Networks Methods for River Retaining Wall Cost Estimation in Grobogan Regency Panuwun, Rizky Tulus; Pratiwi Adi, Henny; Soedarsono, Soedarsono
Journal of Engineering Science and Technology Management (JES-TM) Vol. 5 No. 2 (2025): September 2025
Publisher : Journal of Engineering Science and Technology Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jestm.v5i2.306

Abstract

Grobogan Regency in Central Java Province has a high level of flood risk, so the construction of river retaining walls is an important infrastructure for disaster mitigation. The estimation of construction costs at the early planning stage plays a crucial role in budgeting and technical decision-making. This study aims to compare the accuracy and consistency of two cost estimation approaches: Cost Significant Model (CSM), based on multiple linear regression, and Artificial Neural Networks (ANN) using the backpropagation algorithm. The dataset comprises 42 Bill of Quantity (BoQ) documents (37 training data and 5 testing data), with additional validation conducted through field surveys at seven proposed retaining wall locations. Model performance was evaluated using Mean Absolute Percentage Error (MAPE) to measure accuracy and Bland–Altman Plot to assess consistency. The results indicate that CSM achieved a MAPE value of 1.70%, which is lower than that of ANN, which yielded 2.50%. The Bland–Altman analysis also shows that CSM demonstrates higher consistency, as the linear regression approach allows prediction beyond the training data range, making it more adaptive to actual conditions. In contrast, ANN tends to be constrained within the normalized training data range, reducing its flexibility when encountering new data variations. Therefore, it can be concluded that CSM performs better than ANN in terms of accuracy and consistency in estimating the construction cost of river retaining walls in Grobogan Regency.
Co-Authors Hermin Poedjiastoeti Panuwun, Rizky Tulus Soedarsono Soedarsono Triatmojo, Doni