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All Journal Journal of Computer System and Informatics (JoSYC)
Sindi Fatika Sari
Telkom University, Bandung
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Employee Attrition Prediction Using Feature Selection with Information Gain and Random Forest Classification Sindi Fatika Sari; Kemas Muslim Lhaksmana
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2099

Abstract

Employee attrition is the loss of employees in a company caused by several factors, namely employees resigning, retiring, or other factors. Employee attrition of employees can have a negative impact on a company if it is not handled properly, including decreased productivity. The company also requires more time and effort to recruit and train new employees to fill vacant positions. This attrition prediction aims to help the human resources (HR) department in the company to find out what factors influence the occurrence of employee attrition. This research implements Random Forest while comparing Information Gain, Select K Best, and Recursive Feature Elimination feature selection methods to find which feature selection produces the best performance. The implementation of the aforementioned methods outperforms previous research in terms of accuracy, precision, recall, and f1 scores. In preparing this research, the first author collects data sets, makes programs, and compiles journals. The second author assists the first author in programming and preparing the journal. From the results of the tests that have been carried out, Information Gain produces the highest accuracy value of 89.2%, while Select K Best produces an accuracy value of 87.8% and Recursive Feature Elimination produces an accuracy value of 88.8%.
Co-Authors Kemas Muslim Lhaksmana