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Research Article | Open Access |

Predicting Employee Turnover in IT Industries using Correlation and Chi-Square Visualization

Author 1: Bagus Priambodo Author 2: Yuwan Jumaryadi Author 3: Sarwati Rahayu Author 4: Nur Ani Author 5: Anita Ratnasari Author 6: Umniy Salamah Author 7: Zico Pratama Putra Author 8: Muhamad Otong
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 13, No. 12 · Published 2022

DOI: https://doi.org/10.14569/IJACSA.2022.0131210

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Abstract

Employee turnover in the IT industry is among the highest compared to other industries. Knowing factors that influence the turnover may help reduce this issue in future. One of these factors is job satisfaction, which are composed of two important factors, status and seniority. In this study, the correlation and chi-square visualization are utilized to determine the factors that affect employee turnover. The experiment was carried out to predict turnover using a private IT consultant dataset comparing three classification algorithms (decision tree, Naïve Bayes, and Random Forest). The result shows that job duration and positioning are factors that influence employee turnover in a software company.

Keywords

Employee turnover turnover factors chi-square classification algorithm

How to Cite this Article

Priambodo, B., Jumaryadi, Y., Rahayu, S., Ani, N., Ratnasari, A., Salamah, U., Putra, Z. P., & Otong, M. (2022). Predicting Employee Turnover in IT Industries using Correlation and Chi-Square Visualization. International Journal of Advanced Computer Science and Applications, 13(12). https://doi.org/10.14569/IJACSA.2022.0131210

Priambodo, Bagus, et al.. "Predicting Employee Turnover in IT Industries using Correlation and Chi-Square Visualization." International Journal of Advanced Computer Science and Applications, vol. 13, no. 12, 2022, https://doi.org/10.14569/IJACSA.2022.0131210.

@article{Priambodo2022,
  title     = {Predicting Employee Turnover in IT Industries using Correlation and Chi-Square Visualization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {12},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Bagus Priambodo and Yuwan Jumaryadi and Sarwati Rahayu and Nur Ani and Anita Ratnasari and Umniy Salamah and Zico Pratama Putra and Muhamad Otong},
  doi       = {10.14569/IJACSA.2022.0131210},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131210}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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