Talent Mapping on Human Asset Value (HAV) Matrix Using K-Means Algorithm

Abstract

The Human Resource Department (HRD) in PT.XYZ is responsible in developing and managing human resources. One of the most common management processes is employee performance appraisal. Companies can evaluate the potential and performance of employees’ work through performance appraisal, where HRD can provide information to assist the executive in making administrative decision and employees’ management. However, the results of performance appraisal alone are not sufficientto know the suitable treatment needed by the company in planning the employees’ management and development. To answer these needs, talent mapping on Human Asset Value (HAV) Matrix is needed. HAV is a matrix which determines positions based on employee’s potential and performance. Mapping using the conventional method was done based on determining the coordinate boundaries of each category in the HAV matrix but was still susceptible to the exact set of data within the category boundaries. This research proposes K-means Algorithm to perform talent mapping. Clustering was based on the midpoint of each category in the HAV Matrix. Employees as objects formed clusters according to the closest distance to the centroid. The K-means algorithm iterated until there was no object displacement in each cluster. The resulting cluster was the average object in each category, so the cluster result was adjusted to the human resource conditions in PT.XYZ. The result of this study can be used as a basis in the implementation of talent mapping on Human Resource Information System.
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