Peer Review Report For: Application of K-Means Clustering for Job Applicant Analysis in Construction Firms Using R [version 1; peer review: 1 approved with reservations]

This study applies the K-Means Clustering algorithm to analyse job applicant data at a construction consulting firm. Considering three main variables—AutoCAD drawing skills, planning and supervision report writing skills, and adaptability—this study aims to categorize job applicants into three groups: “Rejected,” “Under Consideration,” and “Accepted.” The clustering process was conducted using the R program with an initial centroid-based approach and visualized through 2D and 3D scatter plots to map the distribution patterns of applicants based on their attributes. The results of the study show that this clustering not only provides a deep understanding of the characteristics of applicants but also supports the company in optimizing data-driven recruitment strategies. Applicants with high scores in all three variables tend to fall into the “Accepted” category, while those with moderate scores are categorized as “Under Consideration.” The visualization of results offers sharper insights into applicant distribution, which can be used to identify training needs or improvements to the selection process. This research contributes to the development of an efficient and objective data-driven recruitment system in the construction sector.