Earticle

초록

영어

This study developed a model to predict employee turnover intention using data from the 2022 Korean Labor & Income Panel Study (KLIPS) with 2471 participants. CopulaGAN and Isolation Forests were employed for data augmentation and variable importance. A logistic regression model using the augmented data achieved an accuracy of 0.80, precision of 0.60, recall of 0.72, and an F1-score of 0.65. Key variables included Job Satisfaction, Wage Satisfaction, Work Hours, Job Stability, and Job-Related Training. The study highlights the potential of these techniques for enhancing turnover prediction and aiding proactive HR strategies.

목차

Abstract
1. Introduction
2. Methods
2.1. Data collection
2.2. Data Preprocessing
2.3. Addressing Data Imbalance
2.4. Detailed Steps of the Algorithm
2.5. Developing and Evaluating the Prediction Model
3. Results
4. Discussion
5. Conclusion
References

키워드

저자

• 변해원 [ Haewon Byeon | Dept. of AI-Software, Inje University, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국기계항공기술학회지(구 한국기계기술학회지) [Journal of the Korean Society of Mechanical and Aviation Technology ]

• 간기

  격월간

• pISSN

  1229-604X

• eISSN

  2508-3805

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 550 DDC 620