Analisis Risiko Keterlambatan pada Proyek Konstruksi Villa Tretes dengan Metode Bayesian Belief Network. (Delay Risk Analysis in the Villa Tretes Construction Project Using the Bayesian Belief Network Method).

Atmojo, Satrio Tri (2026) Analisis Risiko Keterlambatan pada Proyek Konstruksi Villa Tretes dengan Metode Bayesian Belief Network. (Delay Risk Analysis in the Villa Tretes Construction Project Using the Bayesian Belief Network Method). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Project delay is one Of the most common issues encountered in construction projects and may result in cost overruns and disruptions to project objectives. The Villa Tretes Construction Project is exposed to various risk factors that have the potential to cause delays; therefore, a method capble Of comprehensively analyzing the relationships among risks is required. This study ainLf to identify Voject delay riskfactors. analyze their probabilities, and determine the dominant risk factors affecting project delays using the Bayesian Belief Network (BBM) method Data were collected through a literature review, field observations, and questionnaire surveys distributed to prties involved in the project. The collected data were tested for validity and reliability, then analyzed using the mean value, which Was subsequently converted into probabilities as inputfor Bayesian Belief Network modeling using HUGIN software. The BBN model was develorxd based On aruse-and-effect relationships among risk variables that had been validated by exßrts. Ihe analysis results indicate that the probability ofproject delay reaches 84.49% The most dominant riskfactæ affecting project delays is Financial and Administrative Issues with a probability of 84.49% followed by Project Documentation (84.10%), Labor (83.57%), Materials and Equipment (83.56%), Management and Coordination (81.74%), and Environmental Factors (81.43%). The results demonstrate that the Bayesian Belief Network method is capable Of systematically modeling probabilistic relationships among risk factors and supprting risk management decision-making in construction projects.

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