EMPLOYEE VOLUNTARY ATTRITION PREDICTION AT PT.XYZ: ENSEMBLE MACHINE LEARNING APPROACH WITH SOFT VOTING CLASSIFIER

Cagiva Chaedar Bey Lirna; Trimono Trimono; Aviolla Terza Damaliana

doi:10.52436/1.jutif.2024.5.5.2007

Cagiva Chaedar Bey Lirna Data Science, Faculty of Computer, Universitas Pembangunan Nasional "Veteran" Jawa Timur, Indonesia
Trimono Data Science, Faculty of Computer, Universitas Pembangunan Nasional "Veteran" Jawa Timur, Indonesia
Aviolla Terza Damaliana Data Science, Faculty of Computer, Universitas Pembangunan Nasional "Veteran" Jawa Timur, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.5.2007

Keywords: EDA, Employee Retention, Ensemble model, Soft Voting, Voluntary Attrition

Abstract

This research addresses the complexity of employee attrition challenges at PT.XYZ. The main objective is to develop a predictive system for potential voluntary employee attrition by focusing on an in-depth analysis of the factors contributing to attrition at PT.XYZ. The research utilizes data containing information on the job history of PT.XYZ employees from 2018 to 2023. The method employed in the research is a soft voting ensemble classifier model, incorporating SVM, decision tree, and logistic regression, supported by relevant literature. Analysis and exploration of historical data of PT.XYZ employees are conducted to identify key factors influencing employees' decisions to leave the company. Careful data preprocessing is carried out to ensure dataset quality before applying it to the soft voting classifier model. The results of the soft voting classifier modeling used in this research achieve excellent accuracy in both training and testing datasets with respective accuracy percentages of 99% and 98%. Based on the final results of applying the soft voting classifier model, it is expected to provide deep insights and solutions to enhance employee retention at PT.XYZ.

Downloads

Download data is not yet available.

References

S. Sumarita dan M. Muzakki, “Pengaruh Kepemimpinan Transformasional pada Intensi Karyawan Keluar dari Perusahaan,” Indones. Bus. Rev., vol. 6, no. 1, hal. 61, 2023, doi: 10.21632/ibr.6.1.61-70.

W. A. Medyanti dan M. Faisal, “Early Prediction System for Employee Attrition Company ‘XYZ’ Using Support Vector Machine Algorithm,” CESS (Journal Comput. Eng. Syst. Sci., vol. 8, no. 2, hal. 429, 2023, doi: 10.24114/cess.v8i2.46494.

N. Mansor, N. S. Sani, dan M. Aliff, “Machine Learning for Predicting Employee Attrition,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 11, hal. 435–445, 2021, doi: 10.14569/IJACSA.2021.0121149.

D. Jain dan M. B. Buckley, “Evaluation of Employee Attrition by Effective Feature Selection using Hybrid Model of Ensemble Methods MSc Research Project Data Analytics”.

F. K. Alsheref, I. E. Fattoh, dan W. Mead, “Automated Prediction of Employee Attrition Using Ensemble Model Based on Machine Learning Algorithms,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/7728668.

P. Wulansari, B. Meilita, dan Y. Ganesan, “The Effect of Employee Retention Company to Turnover Intention Employee—Case Study on Head Office Lampung Bank,” vol. 117, no. Gcbme 2018, hal. 236–239, 2020, doi: 10.2991/aebmr.k.200131.050.

M. Silalahi, S. Purba, I. K. E. M. Mulyana, M. T. Rambe, dan Abdurohim, Manajemen Sumber Daya Manusia (Strategi Organisasi Modern). 2022.

A. Qutub, A. Al-Mehmadi, M. Al-Hssan, R. Aljohani, dan H. S. Alghamdi, “Prediction of Employee Attrition Using Machine Learning and Ensemble Methods,” Int. J. Mach. Learn. Comput., vol. 11, no. 2, hal. 110–114, 2021, doi: 10.18178/ijmlc.2021.11.2.1022.

T. H. L. Tulus, A. Id Hadiana, dan I. Santikarama, “Sistem Prediksi Awal Terhadap Atrisi Karyawan Menggunakan Algoritma C4.5,” Informatics Digit. Expert, vol. 4, no. 1, hal. 18–24, 2022, doi: 10.36423/index.v4i1.882.

M. A. Al Fatih dan K. M. Lhaksmana, “Prediksi Employee Attrition menggunakan Algoritma Support Vector Machine (SVM),” J. Prediksi Empl. Attrition, vol. 10, no. 2, hal. 1930, 2023.

S. Bhatta, I. U. Zaman, N. Raisa, S. I. Fahim, dan S. Momen, “Machine Learning Approach to Predicting Attrition Among Employees at Work,” Lect. Notes Networks Syst., vol. 502 LNNS, no. July, hal. 285–294, 2022, doi: 10.1007/978-3-031-09076-9_27.

R.-D. Căplescu, M. Ilie, dan V. A. Strat, “Voluntary employee attrition. Descriptive and predictive analysis,” Proc. Int. Conf. Appl. Stat., vol. 1, no. 1, hal. 145–161, 2019, doi: 10.2478/icas-2019-0013.

M. Maulana, “Pengaruh Kompensasi, Pengembangan Karir, dan Kepuasan Kerja terhadap retensi Karyawan (Studi pada PT Suk Sempurna Furnindo),” no. 2005, hal. 1–90, 2020.

D. P. S. Swami, “EMPLOYEE RETENTION STRATEGIES IN IT/ITeS SECTOR: EMPLOYER VIEW,” Interantional J. Sci. Res. Eng. Manag., vol. 06, no. 10, hal. 1–10, 2022, doi: 10.55041/ijsrem16680.

V. Da Poian dkk., “Exploratory data analysis (EDA) machine learning approaches for ocean world analog mass spectrometry,” Front. Astron. Sp. Sci., vol. 10, no. May, hal. 1–17, 2023, doi: 10.3389/fspas.2023.1134141.

P. A. Riyantoko, K. M. Hindrayani, T. M. Fahrudin, dan E. M. Safitri, “Southeast Asia Happiness Report in 2020 Using Exploratory Data Analysis,” Netw. Secur. Inf. Syst., vol. 2, no. 1, hal. 16–21, 2020.

K. Maulida Hindrayani, T. Maulana F, P. Aji R, dan Kartini, “Determining Students Preparation for College Entrance Examinations in Indonesia From Twitter Data Using Exploratory Data Analysis,” Ijconsist Journals, vol. 2, no. 02, hal. 66–70, 2021, doi: 10.33005/ijconsist.v2i02.47.

M. Khairy, T. M. Mahmoud, A. Omar, dan T. Abd El-Hafeez, “Comparative performance of ensemble machine learning for Arabic cyberbullying and offensive language detection,” Lang. Resour. Eval., no. 0123456789, 2023, doi: 10.1007/s10579-023-09683-y.

A. T. Damaliana, T. Trimono, dan D. A. Prasetya, “Ensemble Tree untuk Memprediksi Level Resiko Maternal Mortality di Bangladesh,” … Sains Data, vol. 2022, no. Senada, hal. 24–30, 2022, [Daring]. Tersedia pada: https://prosiding-senada.upnjatim.ac.id/index.php/senada/article/view/36%0Ahttps://prosiding-senada.upnjatim.ac.id/index.php/senada/article/download/36/20

H. B. Kibria, M. Nahiduzzaman, M. O. F. Goni, M. Ahsan, dan J. Haider, “An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI,” Sensors, vol. 22, no. 19, 2022, doi: 10.3390/s22197268.

S. Sumayah, F. Sembiring, dan W. Jatmiko, “Analysis of Sentiment of Indonesian Community on Metaverse Using Support Vector Machine Algorithm,” J. Tek. Inform., vol. 4, no. 1, hal. 143–150, 2023, doi: 10.52436/1.jutif.2023.4.1.417.

Y. B. Lasotte, E. J. Garba, Y. M. Malgwi, dan M. A. Buhari, “An Ensemble Machine Learning Approach for Fake News Detection and Classification Using a Soft Voting Classifier,” Eur. J. Electr. Eng. Comput. Sci., vol. 6, no. 2, hal. 1–7, 2022, doi: 10.24018/ejece.2022.6.2.409.

NAGİHAN TAŞKIRAN, “A RECOMMENDATION APPROACH FOR EMPLOYEE RETENTION BY USING A NEW FEATURE SELECTION STRATEGY,” no. September, hal. 31–41, 2023.

A. Rana dan R. Pandey, “A review of popular decision tree algorithms in data mining,” Asian J. Multidimens. Res., vol. 10, no. 10, hal. 230–237, 2021, doi: 10.5958/2278-4853.2021.00837.5.

F. H. Wardhani dan K. M. Lhaksmana, “Predicting Employee Attrition Using Logistic Regression With Feature Selection,” Sinkron, vol. 7, no. 4, hal. 2214–2222, 2022, doi: 10.33395/sinkron.v7i4.11783.