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Employee Attrition and Performance Prediction using Univariate ROC feature selection and Random Forest
Corresponding Author(s) : Farrikh Alzami
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 6, No. 4, November 2021
Abstract
Each company applies a contract extension to assess the performance of its employees. Employees with good performance in the company are entitled to future contracts within a certain period of time. In a pandemic time, many companies have made decisions to carry out WFH (Work from Home) activities even to Termination (Attrition) of Employment. The company's performance cannot be stable if in certain fields it does not meet the criteria required by the company. Thus, due to many things to consider in contract extension, we are proposed feature selection steps such as duplicate features, correlated features and Univariate Receiver Operating Characteristics curve (ROC) to reduce features from 35 to 21 Features. Then, after we obtained the best features, we applied into Decision Trees and Random Forest. By optimizing parameter selection using parameter grid, the research concluded that Random Forest with feature selection can predict Employee Attrition and Performance by obtain accuracy 79.16%, Recall 76% and Precision 82,6%. Thus with those result, we can conclude that we can obtain better prediction using 21 features for employee attrition and performance which help the higher management in making decisions.
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- P. Altıok, “Applicable vision, mission and the effects of strategic management on crisis resolve,” Procedia - Soc. Behav. Sci., vol. 24, pp. 61–71, 2011. https://doi.org/10.1016/j.sbspro.2011.09.057
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- S. M. Hamidi, “Performance Appraisal and Its Effects on Employees Motivation: A Case Study of Afghan Wireless Communications in Kabul,” SSRN Electron. J., 2019. https://dx.doi.org/10.2139/ssrn.3426851
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- K. Haldorai, W. G. Kim, S. G. Pillai, T. (Eliot) Park, and K. Balasubramanian, “Factors affecting hotel employees’ attrition and turnover: Application of pull-push-mooring framework,” Int. J. Hosp. Manag., vol. 83, pp. 46–55, Oct. 2019. https://doi.org/10.1016/j.ijhm.2019.04.003
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- M. C. E. Simsekler, A. Qazi, M. A. Alalami, S. Ellahham, and A. Ozonoff, “Evaluation of patient safety culture using a random forest algorithm,” Reliab. Eng. Syst. Saf., vol. 204, p. 107186, Dec. 2020. https://doi.org/10.1016/j.ress.2020.107186
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References
P. Altıok, “Applicable vision, mission and the effects of strategic management on crisis resolve,” Procedia - Soc. Behav. Sci., vol. 24, pp. 61–71, 2011. https://doi.org/10.1016/j.sbspro.2011.09.057
A. A. Davidescu, S.-A. Apostu, A. Paul, and I. Casuneanu, “Work Flexibility, Job Satisfaction, and Job Performance among Romanian Employees—Implications for Sustainable Human Resource Management,” Sustainability, vol. 12, no. 15, p. 6086, Jul. 2020. https://doi.org/10.3390/su12156086
S. M. Hamidi, “Performance Appraisal and Its Effects on Employees Motivation: A Case Study of Afghan Wireless Communications in Kabul,” SSRN Electron. J., 2019. https://dx.doi.org/10.2139/ssrn.3426851
V. V. Saradhi and G. K. Palshikar, “Employee churn prediction,” Expert Syst. Appl., vol. 38, no. 3, pp. 1999–2006, Mar. 2011. https://doi.org/10.1016/j.eswa.2010.07.134
V. Shapoval, “Organizational injustice and emotional labor in the hospitality industry: A theoretical review,” Int. J. Hosp. Manag., vol. 83, pp. 56–64, Oct. 2019. https://doi.org/10.1016/j.ijhm.2019.04.002
K. Haldorai, W. G. Kim, S. G. Pillai, T. (Eliot) Park, and K. Balasubramanian, “Factors affecting hotel employees’ attrition and turnover: Application of pull-push-mooring framework,” Int. J. Hosp. Manag., vol. 83, pp. 46–55, Oct. 2019. https://doi.org/10.1016/j.ijhm.2019.04.003
D. J. Madigan and L. E. Kim, “Towards an understanding of teacher attrition: A meta-analysis of burnout, job satisfaction, and teachers’ intentions to quit,” Teach. Teach. Educ., vol. 105, p. 103425, Sep. 2021. https://doi.org/10.1016/j.tate.2021.103425
A. Oussous, F.-Z. Benjelloun, A. Ait Lahcen, and S. Belfkih, “Big Data technologies: A survey,” J. King Saud Univ. - Comput. Inf. Sci., vol. 30, no. 4, pp. 431–448, Oct. 2018. https://doi.org/10.1016/j.jksuci.2017.06.001
A. Purnomo, M. A. Barata, M. A. Soeleman, and F. Alzami, “Adding feature selection on Naïve Bayes to increase accuracy on classification heart attack disease,” J. Phys. Conf. Ser., vol. 1511, p. 012001, Apr. 2020. https://doi.org/10.1088/1742-6596/1511/1/012001
F. Alzami, E. D. Udayanti, D. P. Prabowo, and R. A. Megantara, “Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, pp. 235–242, Aug. 2020. https://doi.org/10.22219/kinetik.v5i3.1066
Purwanto, Sunardi, F. T. Julfia, and A. Paramananda, “Hybrid model of ARIMA-linear trend model for tourist arrivals prediction model in Surakarta City, Indonesia,” in AIP Conference Proceedings, 2019, vol. 2114, p. 060010. https://doi.org/10.1063/1.5112481
P. X. Xiang Liu, “Feature Selection using Bootstrapped ROC Curves,” J. Proteomics Bioinform., vol. s9, 2014.
A. Bommert, X. Sun, B. Bischl, J. Rahnenführer, and M. Lang, “Benchmark for filter methods for feature selection in high-dimensional classification data,” Comput. Stat. Data Anal., vol. 143, p. 106839, Mar. 2020. https://doi.org/10.1016/j.csda.2019.106839
E. B. Nkemnole and O. Abass, “A t-distribution based particle filter for univariate and multivariate stochastic volatility models,” J. Niger. Math. Soc., vol. 34, no. 2, pp. 227–242, Aug. 2015. http://dx.doi.org/10.1016%2Fj.jnnms.2014.11.002
A. De Caigny, K. Coussement, and K. W. De Bock, “A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees,” Eur. J. Oper. Res., vol. 269, no. 2, pp. 760–772, Sep. 2018. https://doi.org/10.1016/j.ejor.2018.02.009
M. C. E. Simsekler, A. Qazi, M. A. Alalami, S. Ellahham, and A. Ozonoff, “Evaluation of patient safety culture using a random forest algorithm,” Reliab. Eng. Syst. Saf., vol. 204, p. 107186, Dec. 2020. https://doi.org/10.1016/j.ress.2020.107186
M. Kuhn and K. Johnson, Applied predictive modeling. New York, NY: Springer New York, 2013.
A. Fernández, S. García, M. Galar, R. C. Prati, B. Krawczyk, and F. Herrera, Learning from Imbalanced Data Sets. Cham: Springer International Publishing, 2018.
S. L. Salzberg, “C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993,” Mach. Learn., vol. 16, no. 3, pp. 235–240, Sep. 1994. https://doi.org/10.1007/BF00993309
L. Breiman, “Random Forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001. https://doi.org/10.1023/A:1010933404324