Analisis K-Means Clustering pada Data Penempatan Karyawan di PT. Guna Cipta Prima Berbasis Web - Sella Sella; Yohanni Syahra (Journal of Science, Technology, and Innovation, 2026)
The advancement of information technology has made a significant contribution to decision-making processes, particularly in human resource management. One of the common challenges faced by organizations is the difficulty in determining employee placement objectively based on their competencies and performance. This study aims to implement the K-Means Clustering algorithm to classify employee data at PT Guna Cipta Prima, develop a web-based system using PHP and MySQL, and analyze the resulting clusters. The data used are real-world data consisting of job attributes such as security and cleaning service. The clustering process is conducted using three categories: high, medium, and low performance. The results indicate that the developed system is capable of effectively implementing the K-Means algorithm and producing clusters that align with the characteristics of the data. A Silhouette Coefficient value of 0.67 demonstrates a reasonably good level of clustering validity. Therefore, this system can assist the Human Resources Department (HRD) in conducting employee placement analysis in a more efficient, objective, and data-driven manner, thereby supporting more accurate decision-making.