Prediction Of Project Schedule Performance Index for Trans South Java Road Project Lot 7 Blitar Regency Using Bayesian Network

Abstract

The south coast of Indonesia has considerable potential and competitiveness in the field of tourism. This encourages the government to strive to improve tourism infrastructure through the construction of the South Coast Road. In its implementation, project reporting must be measured and controlled to assist project management in identifying problems and factors that affect project activities. This research aims to identify the most influential factors on Schedule Performance Index and develop a prediction model using the Bayesian Network. 5 main factors that affect project performance, such as Heavy Equipment, materials, Implementation and Work Relations, Labor, and Environment are used to are set 13 scenarios to detect the behavior of each factor appropriately. The factor is confirmed to the respondent to ensure that the factor occurs in the project. The study's results obtained Bayesian Network approach can be used to assess the condition of the JLS Lot 7 Blitar Schedule Performance Index (SPI) with an accuracy of around 80%. The dominant factor affecting SPI is the condition of the Heavy Equipment. The condition of the Heavy Equipment will affect the condition of SPI so closely that the contractor must maintain the performance of the heavy equipment so that the project performance is always in good condition. The identification results are expected to help in better decision-making and project risk management.