Earticle

초록

영어

Employee placement is one of the vital functions of HR, which aligns the employee's skill with the organization's requirements. Traditional methods of placement have many shortcomings: skill mismatching, bias, and underutilization or misutilization of resources. The study uses machine learning (ML) to these problems by evaluating three algorithms-AdaBoost, support vector machine (SVM), and CatBoost-with demographic and job-related data from Kaggle. Results indicate that the maximum accuracy AdaBoost reached was 86%, then SVM with 81.4%, followed by CatBoost at 79%. These findings point to the reliability of AdaBoost in structured data and emphasize the potential that ML has for improving HR efficiency, employee satisfaction, and retention.

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. METHODOLOGY
A. Dataset Employee Future Prediction
B. Cat Boost
C. AdaBoost
D. SVM
IV. SIMULATION AND RESULTS
V. CONCLUSION
REFERENCES

저자

• Arif Wicaksono Septyanto [ Information Systems Institut Teknologi Kalimantan Balikpapan, Indonesia ]
• Muhammad Usman [ School of Computer Science National College of Business Administration & Economics, Lahore 54000, Pakistan ]
• Esham Fatima [ School of Computer Science National College of Business Administration & Economics, Lahore 54000, Pakistan ]
• Gulfaraz Anis [ School of Computer Science National College of Business Administration & Economics, Lahore 54000, Pakistan ]
• Ali Zaman Malik [ School of Computer Science National College of Business Administration & Economics, Lahore 54000, Pakistan ]
• Ubaid Ullah [ Faculty of Information and Communication Technology Universiti Tunku Abdul Rehman Malaysia ]

참고문헌