Talent Acquisition Implementation with People Analytic Approach

Abstract

The current human resource (HR) fulfillment conditions in this company are still quite low. This can be seen from the percentage of HR fulfillment of approximately 60% of the total HR needs. The strategy of fulfilling human resources through the recruitment and selection process must be done quickly and optimally. The problem that arises is related to the optimization of the talent acquisition process carried out, so that the results obtained are in accordance with the target and have quality that meets the required. In this study, data analysis was used using the random forest method. The method is used to develop a model that can predict the pass level of participants in recruitment and selection quickly and precisely in accordance with the profile of each participant, and can provide insight on the projected achievement of individual performance on each participant if passed at the company, to assist management in making decisions about the participants accepted in the recruitment and selection process. The data population used is data on recruitment and selection participants in 2018. To carry out the process of predicting the graduation rate of prospective employees, data for prospective employees who register for the recruitment and selection process will be used with a total of 17,294 people. The analytical tool in this study uses a people analytic approach. The conclusion of this study is that making people analytics on the process of talent acquisition can be done using the Random Forest Classification method. This method aims to determine the class of each predicted data. Modeling has been made to predict performance achievements, but the performance of the model is still not showing the level of significance in accordance with the standard level of confidence, which is still below 0.05.