Gradient Tree Boosting for HR Talent Management Application

Talent Management is a stage process of a company to find, sort, improve, and retain the best talent of employees for the needs of the company or agency. A company recommends or requires Talent Management to go through an open recruitment process by taking a final test and selection, namely an interview or a recommendation. However, the weakness of the current system is still dominant using a recommendation system that contains elements of the subjectivity of related parties which is not by the results determined from the capacity test, employee performance, and psychological tests. From these weaknesses, in this study, a machine learning approach with the Gradient Tree Boosting (GTB) method was developed to recommend the position of structural officials. In this study, we use a talent management dataset from a university in Indonesia. The experimental result shows the promising performance of our proposed method.