Performance Analysis of Property Construction Vendors Using the Analytical Hierarchy Process and Decision Tree Methods

Abstract

Vendor performance evaluation in property construction projects is often dominated by subjective assessments, which may lead to inconsistent decision-making. This study aims to analyse the performance of property construction vendors at PT PELNI (Persero) using the Analytical Hierarchy Process (AHP) and the C4.5 Decision Tree method. Evaluation criteria and sub-criteria were identified through expert interviews, after which priority weights were calculated using AHP. The C4.5 Decision Tree algorithm was then employed to classify vendor performance based on historical data. To address class imbalance in the training dataset, the Synthetic Minority Oversampling Technique (SMOTE) was applied. The results identified four evaluation criteria and ten sub-criteria, with Quality emerging as the most influential criterion, receiving the highest priority weight of 0.432. The C4.5 Decision Tree model achieved an accuracy of 88.89% and a recall of 83.33%, generating seven interpretable classification rules. A comparison of the two methods indicates that AHP is effective for determining the priority weights of evaluation indicators and ranking vendors, while the Decision Tree method effectively identifies classification patterns from historical performance data. The integration of these methods provides a more objective and consistent vendor evaluation framework, supporting informed decision-making in vendor selection and performance assessment at PT PELNI (Persero).