| Abstrakt: |
Employee turnover is a problem that affects every organization, whether in government or private sector. Employee attrition leads to high costs for any organization, especially in terms of training. In Royal Malaysia Police Force (PDRM), the optional retirement rate is higher compared to those that retire at retirement age. This study will focus on the factors that cause PDRM officers to choose optional retirement using Machine Learning (ML) techniques. In this study, k-prototype cluster analysis, Random Forest, and text analytics were performed for various analysis purposes. The results show that an officer's age is the primary motivator for electing optional retirement over mandatory retirement. Various health issues cause lower productivity and enthusiasm in performing tasks in their work. The work placement, the job ranks, the remainder service year, and the time period of the last promotion is among those identified crucial factors that contributed towards early retirement in PDRM. Since family concerns are frequently cited as a reason for retirees choosing early retirement, the work-life balanced in the police force profession was also noted as another early retirement factor. The findings of this study may assist PDRM in revamping the career in the police force so that the problem of high attrition rate can be curbed and also make the profession more attractive, hence attracting more people to join the police force. [ABSTRACT FROM AUTHOR] |
