Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2023, , 344 - 353, 27.06.2023
https://doi.org/10.17798/bitlisfen.1196174

Öz

Kaynakça

  • [1] S. M. Crow, S. A. Smith, and S. J. Hartman, “Attrition in nursing: Perspectives from the national survey of college graduates,” Health Care Manag. (Frederick), vol. 24, no. 4, pp. 336–346, 2005.
  • [2] W. P. Durow and B. Brock, “The retention and attrition of catholic school principals,” Journal of Catholic Education, vol. 8, no. 2, 2004.
  • [3] P. Weldon, “Early career teacher attrition in Australia: evidence, definition, classification and measurement,” Aust. J. Educ., vol. 62, no. 1, pp. 61–78, 2018.
  • [4] N. Pandey and G. Kaur, “Factors influencing employee attrition in Indian ITeS call centres,” Int. J. Indian Cult. Bus. Manage., vol. 4, no. 4, p. 419, 2011.
  • [5] L. Kanteh and A. Gibba, “A study on employees’ attrition in public and private institutions in the Gambia, 2007-2017,” Arabian J Bus Manager Review, 2007.
  • [6] D. Kennedy, “Attrition rates of mature engineers,” Eng. Manag. J., vol. 18, no. 3, pp. 36–40, 2006.
  • [7] C. Marcelo, Does Employee Training and Development Programs Mitigate Attrition in the Millennial Workforce? 2022.
  • [8] N. Aariya, “A study on the impact of maternity benefit on teacher’s career and increase in post maternity attrition rate in India,” Journal for Educators, Teachers and Trainers, vol. 13, no. 2, pp. 207–214, 2022.
  • [9] S. Bhardwaj and A. Singh, “Factors affecting employee attrition among engineers and non-engineers in manufacturing industry,” Bus. IT, vol. VII, no. 2, pp. 26–34, 2017.
  • [10] S. Barpanda and S. Athira, “Cause of Attrition in an Information Technology-Enabled Services Company: A Triangulation Approach,” International Journal of Human Capital and Information Technology Professionals (IJHCITP), vol. 13, no. 1, pp. 1–22, 2022.
  • [11] D. Ayar, M. A. Karaman, and R. Karaman, “Work-life balance and mental health needs of health professionals during COVID-19 pandemic in turkey,” Int. J. Ment. Health Addict., vol. 20, no. 1, pp. 639–655, 2022.
  • [12] C. Calderwood, R. Breaux, L. L. Ten Brummelhuis, T. Mitropoulos, and C. S. Swanson, “When daily challenges become too much during COVID-19: Implications of family and work demands for work-life balance among parents of children with special needs,” J. Occup. Health Psychol., vol. 27, no. 5, pp. 516–527, 2022.
  • [13] L. Vyas, “New normal” at work in a post-COVID world: work-life balance and labor markets,” Policy and Society, vol. 41, no. 1, pp. 155–167, 2022.
  • [14] K. K. Mohbey, “Employee’s attrition prediction using machine learning approaches,” in Machine Learning and Deep Learning in Real-Time Applications, IGI Global, 2020, pp. 121–128.
  • [15] R. Jain and A. Nayyar, “Predicting employee attrition using XGBoost machine learning approach,” in 2018 International Conference on System Modeling & Advancement in Research Trends (SMART), 2018.
  • [16] S. Najafi-Zangeneh, N. Shams-Gharneh, A. Arjomandi-Nezhad, and S. Hashemkhani Zolfani, “An improved machine learning-based employees attrition prediction framework with emphasis on feature selection,” Mathematics, vol. 9, no. 11, p. 1226, 2021.
  • [17] N. El-Rayes, M. Fang, M. Smith, and S. M. Taylor, “Predicting employee attrition using tree-based models,” Int. J. Organ. Anal., vol. ahead-of-print, no. ahead-of-print, 2020.
  • [18] C. Zhang and Y. Ma, Eds., Ensemble machine learning: methods and applications. Springer Science & Business Media, 2012.
  • [19] R. Polikar, “Ensemble Learning,” in Ensemble Machine Learning, Boston, MA: Springer US, 2012, pp. 1–34.
  • [20] S. Buyrukoglu, “Improvement of Machine Learning Models’ Performances based on Ensemble Learning for the detection of Alzheimer Disease,” in 2021 6th International Conference on Computer Science and Engineering (UBMK), 2021.
  • [21] S. Buyrukoğlu and S. Savaş, “Stacked-based ensemble machine learning model for positioning footballer,” Arab. J. Sci. Eng., 2022.
  • [22] S. Buyrukoğlu, “New hybrid data mining model for prediction of Salmonella presence in agricultural waters based on ensemble feature selection and machine learning algorithms,” J. Food Saf., vol. 41, no. 4, 2021.
  • [23] A. Akbas and S. Buyrukoglu, “Stacking Ensemble Learning-Based Wireless Sensor Network Deployment Parameter Estimation,” Arabian Journal for Science and Engineering, pp. 1–10, 2022.
  • [24] K. Radha and M. Rohith, “An experimental analysis of work-life balance among the employees using machine learning classifiers,” Int. J. Comput. Trends Technol., vol. 69, no. 4, pp. 39–48, 2021.
  • [25] A. Pawlicka, M. Pawlicki, R. Tomaszewska, M. Choraś, and R. Gerlach, “Innovative machine learning approach and evaluation campaign for predicting the subjective feeling of work-life balance among employees,” PLoS One, vol. 15, no. 5, p. e0232771, 2020.
  • [26] J. Brownlee, “Ensemble Learning Algorithms With Python: Make Better Prediction with Bagging, Boosting, and Stacking,” Machine Learning Mastery, 2021.
  • [27] J. Brownlee, “A Gentle Introduction to Ensemble Learning Algorithms,” Machine Learning Mastery, 2021.
  • [28] X. Wang, B. Yu, A. Ma, C. Chen, B. Liu, and Q. Ma, “Protein-protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique,” Bioinformatics, vol. 35, no. 14, pp. 2395–2402, 2019.
  • [29] A. Navlani, “Support vector machine classification in scikit-learn,” Medium, 16-Aug-2020. [Online]. Available: https://avinashnavlani.medium.com/support-vector-machine-classification-in-scikit-learn-3800bc4979ce. [Accessed: 05-Mar-2023].
  • [30] S. Suthaharan, “Big Data Essentials,” in Machine Learning Models and Algorithms for Big Data Classification, Boston, MA: Springer US, 2016, pp. 17–29.
  • [31] M. Pal, “Random forest classifier for remote sensing classification,” Int. J. Remote Sens., vol. 26, no. 1, pp. 217–222, 2005.
  • [32] B. Suchetana, B. Rajagopalan, and J. Silverstein, “Assessment of wastewater treatment facility compliance with decreasing ammonia discharge limits using a regression tree model,” Sci. Total Environ., vol. 598, pp. 249–257, 2017.
  • [33] A. Natekin and A. Knoll, “Gradient boosting machines, a tutorial,” Front. Neurorobot., vol. 7, 2013.
  • [34] D. Chakraborty, H. Elhegazy, H. Elzarka, and L. Gutierrez, “A novel construction cost prediction model using hybrid natural and light gradient boosting,” Adv. Eng. Inform., vol. 46, no. 101201, p. 101201, 2020.
  • [35] M. Toğaçar, B. Ergen, and V. Tümen, “Use of dominant activations obtained by processing OCT images with the CNNs and slime mold method in retinal disease detection,” Biocybern. Biomed. Eng., vol. 42, no. 2, pp. 646–666, 2022.
  • [36] N. Mansor, N. S. Sani, and M. Aliff, “Machine learning for predicting employee attrition,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 11, 2021.
  • [37] N. Darapaneni et al., “A detailed analysis of AI models for predicting employee attrition risk,” in 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), 2022.

Predicting the Work-Life Balance of Employees Based on the Ensemble Learning Method

Yıl 2023, , 344 - 353, 27.06.2023
https://doi.org/10.17798/bitlisfen.1196174

Öz

Work-life has a great impact on other parts of people’s lives. The effort made in the workspace would cause attrition, exhaustion, and health problems. Employers need to take necessary measures to keep employees motivated by helping them balance work and personal lives. Employers could use many different techniques to measure their workers’ work-life balance and analyze them such as questionnaires and machine learning techniques. This research has been carried out to cluster the employees based on the level of attrition using effort and work-life balance parameters. In order to accomplish this, machine learning including ensemble learning techniques is used. An ensemble learning algorithm, random forest, performed almost the same as the support vector machine with the highest score, 95%. Almost all algorithms whether or not they are a member of ensemble learning performed with the f-score of 86%. However, one of the ensemble learning models, xGBoost, performed poorly with the lowest f-score of 69%. All algorithms predicted the lowest and the highest work-life balance scores, however, confused predicting the middle scores (class 2 and class 3).

Kaynakça

  • [1] S. M. Crow, S. A. Smith, and S. J. Hartman, “Attrition in nursing: Perspectives from the national survey of college graduates,” Health Care Manag. (Frederick), vol. 24, no. 4, pp. 336–346, 2005.
  • [2] W. P. Durow and B. Brock, “The retention and attrition of catholic school principals,” Journal of Catholic Education, vol. 8, no. 2, 2004.
  • [3] P. Weldon, “Early career teacher attrition in Australia: evidence, definition, classification and measurement,” Aust. J. Educ., vol. 62, no. 1, pp. 61–78, 2018.
  • [4] N. Pandey and G. Kaur, “Factors influencing employee attrition in Indian ITeS call centres,” Int. J. Indian Cult. Bus. Manage., vol. 4, no. 4, p. 419, 2011.
  • [5] L. Kanteh and A. Gibba, “A study on employees’ attrition in public and private institutions in the Gambia, 2007-2017,” Arabian J Bus Manager Review, 2007.
  • [6] D. Kennedy, “Attrition rates of mature engineers,” Eng. Manag. J., vol. 18, no. 3, pp. 36–40, 2006.
  • [7] C. Marcelo, Does Employee Training and Development Programs Mitigate Attrition in the Millennial Workforce? 2022.
  • [8] N. Aariya, “A study on the impact of maternity benefit on teacher’s career and increase in post maternity attrition rate in India,” Journal for Educators, Teachers and Trainers, vol. 13, no. 2, pp. 207–214, 2022.
  • [9] S. Bhardwaj and A. Singh, “Factors affecting employee attrition among engineers and non-engineers in manufacturing industry,” Bus. IT, vol. VII, no. 2, pp. 26–34, 2017.
  • [10] S. Barpanda and S. Athira, “Cause of Attrition in an Information Technology-Enabled Services Company: A Triangulation Approach,” International Journal of Human Capital and Information Technology Professionals (IJHCITP), vol. 13, no. 1, pp. 1–22, 2022.
  • [11] D. Ayar, M. A. Karaman, and R. Karaman, “Work-life balance and mental health needs of health professionals during COVID-19 pandemic in turkey,” Int. J. Ment. Health Addict., vol. 20, no. 1, pp. 639–655, 2022.
  • [12] C. Calderwood, R. Breaux, L. L. Ten Brummelhuis, T. Mitropoulos, and C. S. Swanson, “When daily challenges become too much during COVID-19: Implications of family and work demands for work-life balance among parents of children with special needs,” J. Occup. Health Psychol., vol. 27, no. 5, pp. 516–527, 2022.
  • [13] L. Vyas, “New normal” at work in a post-COVID world: work-life balance and labor markets,” Policy and Society, vol. 41, no. 1, pp. 155–167, 2022.
  • [14] K. K. Mohbey, “Employee’s attrition prediction using machine learning approaches,” in Machine Learning and Deep Learning in Real-Time Applications, IGI Global, 2020, pp. 121–128.
  • [15] R. Jain and A. Nayyar, “Predicting employee attrition using XGBoost machine learning approach,” in 2018 International Conference on System Modeling & Advancement in Research Trends (SMART), 2018.
  • [16] S. Najafi-Zangeneh, N. Shams-Gharneh, A. Arjomandi-Nezhad, and S. Hashemkhani Zolfani, “An improved machine learning-based employees attrition prediction framework with emphasis on feature selection,” Mathematics, vol. 9, no. 11, p. 1226, 2021.
  • [17] N. El-Rayes, M. Fang, M. Smith, and S. M. Taylor, “Predicting employee attrition using tree-based models,” Int. J. Organ. Anal., vol. ahead-of-print, no. ahead-of-print, 2020.
  • [18] C. Zhang and Y. Ma, Eds., Ensemble machine learning: methods and applications. Springer Science & Business Media, 2012.
  • [19] R. Polikar, “Ensemble Learning,” in Ensemble Machine Learning, Boston, MA: Springer US, 2012, pp. 1–34.
  • [20] S. Buyrukoglu, “Improvement of Machine Learning Models’ Performances based on Ensemble Learning for the detection of Alzheimer Disease,” in 2021 6th International Conference on Computer Science and Engineering (UBMK), 2021.
  • [21] S. Buyrukoğlu and S. Savaş, “Stacked-based ensemble machine learning model for positioning footballer,” Arab. J. Sci. Eng., 2022.
  • [22] S. Buyrukoğlu, “New hybrid data mining model for prediction of Salmonella presence in agricultural waters based on ensemble feature selection and machine learning algorithms,” J. Food Saf., vol. 41, no. 4, 2021.
  • [23] A. Akbas and S. Buyrukoglu, “Stacking Ensemble Learning-Based Wireless Sensor Network Deployment Parameter Estimation,” Arabian Journal for Science and Engineering, pp. 1–10, 2022.
  • [24] K. Radha and M. Rohith, “An experimental analysis of work-life balance among the employees using machine learning classifiers,” Int. J. Comput. Trends Technol., vol. 69, no. 4, pp. 39–48, 2021.
  • [25] A. Pawlicka, M. Pawlicki, R. Tomaszewska, M. Choraś, and R. Gerlach, “Innovative machine learning approach and evaluation campaign for predicting the subjective feeling of work-life balance among employees,” PLoS One, vol. 15, no. 5, p. e0232771, 2020.
  • [26] J. Brownlee, “Ensemble Learning Algorithms With Python: Make Better Prediction with Bagging, Boosting, and Stacking,” Machine Learning Mastery, 2021.
  • [27] J. Brownlee, “A Gentle Introduction to Ensemble Learning Algorithms,” Machine Learning Mastery, 2021.
  • [28] X. Wang, B. Yu, A. Ma, C. Chen, B. Liu, and Q. Ma, “Protein-protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique,” Bioinformatics, vol. 35, no. 14, pp. 2395–2402, 2019.
  • [29] A. Navlani, “Support vector machine classification in scikit-learn,” Medium, 16-Aug-2020. [Online]. Available: https://avinashnavlani.medium.com/support-vector-machine-classification-in-scikit-learn-3800bc4979ce. [Accessed: 05-Mar-2023].
  • [30] S. Suthaharan, “Big Data Essentials,” in Machine Learning Models and Algorithms for Big Data Classification, Boston, MA: Springer US, 2016, pp. 17–29.
  • [31] M. Pal, “Random forest classifier for remote sensing classification,” Int. J. Remote Sens., vol. 26, no. 1, pp. 217–222, 2005.
  • [32] B. Suchetana, B. Rajagopalan, and J. Silverstein, “Assessment of wastewater treatment facility compliance with decreasing ammonia discharge limits using a regression tree model,” Sci. Total Environ., vol. 598, pp. 249–257, 2017.
  • [33] A. Natekin and A. Knoll, “Gradient boosting machines, a tutorial,” Front. Neurorobot., vol. 7, 2013.
  • [34] D. Chakraborty, H. Elhegazy, H. Elzarka, and L. Gutierrez, “A novel construction cost prediction model using hybrid natural and light gradient boosting,” Adv. Eng. Inform., vol. 46, no. 101201, p. 101201, 2020.
  • [35] M. Toğaçar, B. Ergen, and V. Tümen, “Use of dominant activations obtained by processing OCT images with the CNNs and slime mold method in retinal disease detection,” Biocybern. Biomed. Eng., vol. 42, no. 2, pp. 646–666, 2022.
  • [36] N. Mansor, N. S. Sani, and M. Aliff, “Machine learning for predicting employee attrition,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 11, 2021.
  • [37] N. Darapaneni et al., “A detailed analysis of AI models for predicting employee attrition risk,” in 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), 2022.
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Vedat Tümen 0000-0003-0271-216X

Ayşe Saliha Sunar 0000-0002-0836-5616

Erken Görünüm Tarihi 27 Haziran 2023
Yayımlanma Tarihi 27 Haziran 2023
Gönderilme Tarihi 31 Ekim 2022
Kabul Tarihi 17 Nisan 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

IEEE V. Tümen ve A. S. Sunar, “Predicting the Work-Life Balance of Employees Based on the Ensemble Learning Method”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, c. 12, sy. 2, ss. 344–353, 2023, doi: 10.17798/bitlisfen.1196174.



Bitlis Eren Üniversitesi
Fen Bilimleri Dergisi Editörlüğü

Bitlis Eren Üniversitesi Lisansüstü Eğitim Enstitüsü        
Beş Minare Mah. Ahmet Eren Bulvarı, Merkez Kampüs, 13000 BİTLİS        
E-posta: fbe@beu.edu.tr