Abstract:
"
In this connected era, High amount of employee turnover has been one of the major
problem that is faced by HR professionals of companies. Employees are critical
resources of the organizations, and retaining them has been an critical success factor
for the companies’ success and continues growth. High turnover could be an indication
that employees are unsatisfactory with the workspace. To compete in the workforce,
companies are continuously working hard to recognize them self as employee friendly
company to attract more employees. Employee’s satisfaction is one of the main
indicator to identify the organization value.
The primary requirement to predict the employee turnover is data. The organization
have started heavily invest in the data management and information systems, including
HR Information Systems (HRIS). They contain massive amount of data related to
employees. Utilizing data visualization techniques and machine learning techniques,
the management can figure out implicit patterns to predict turnover.
This study used combination of approaches to build an industry level prediction system
such as: Hyper parameter optimization, imbalance correction and hybrid model. As the
result of experimenting 7 different types of machine learning algorithm, the hybrid
model was composed of Random Forest and Extreme Gradient Boosting Algorithms.
The hybrid model proposed has been able to achieve accuracy of 97.83% outperforming
the single models.
"
