Makine Öğrenmesi Teknikleri ile İşten Ayrılacak Personelin Tahminlenmesi ve Tekniklerin Performanslarının Karşılaştırılması

Yıl 2024, Cilt: 15 Sayı: 4, 807 - 816

Batuhan Bilenler , Sait Gül , Tamer Uçar

https://doi.org/10.24012/dumf.1517176

Öz

Şirketlerin sürdürülebilir başarısı için yetenekli insan kaynağını şirkette tutundurmak oldukça önemlidir. Bu çalışmada, araştırma yapmak amacıyla açık kaynak olarak paylaşılmış bir şirketin personel verileri kullanılarak işten ayrılacak olan personelin tahminlenebilmesi amaçlanmıştır. Gradient Boosting Tree, Random Forest Trees, XGBoosting Regresyon teknikleri ile tahminleme yapılmıştır. Değerlendirme metrikleri olan ortalama mutlak hata (mean absolute error), R² Skoru, ortalama kare hatası (mean squared error) ve düzeltilmiş R² (Adjusted R²) değerleri karşılaştırılmış olup 3 modelde de anlamlı ölçüde tahminleme yapılabilindiği sonucuna ulaşılmıştır. Ortalama hata skorları birbirine oldukça yakın olduğu için R² değeri 1’e en yakın olan Random Forest tekniği üzerinden özellik önemi çıkarılmıştır. İncelenen öznitelikler arasında, işten ayrılmayı etkileyen en önemli özniteliğin personel tatmini olduğu görülmüştür. Makine öğrenmesi tekniklerinin insan kaynakları alanında kullanımının, şirket içerisindeki yetenekli personeli şirkete tutundurma stratejilerini belirleme anlamında insan kaynakları yöneticilerine oldukça faydalı çıktılar üretebileceği düşünülmektedir.

Anahtar Kelimeler

personel kayıp tahmini, makine öğrenmesi, insan kaynakları analitiği, veri analizi

Kaynakça

• [1] S. Reşitoğlu, “Yetkinlik Bazlı Performans Değerlendirme ve Çalışan Memnuniyeti- Bir Uygulama,” Yüksek Lisans Tezi, Dokuz Eylül Üniversitesi, İzmir, 2011.
• [2] C. Yalçın, “Müşteri Kayıp Analizi (Customer Churn Analysis),” YBS Ansiklopedi, vol. 7, 2019.
• [3] V. Gülpınar, “Yapay Sinir Ağları ve Sosyal Ağ Analizi Yardımı ile Türk Telekomünikasyon Piyasasında Müşteri Kaybı Analizi,” Marmara Üniversitesi İktisadi İdari Bilimler Dergisi, vol. 34, no. 1, pp. 331-350, 2013.
• [4] R. S. Shankar, J. Rajanikanth, V. V. Sivaramaraju, and K. V. S. S. R. Murthy, “Prediction of Employee Attrition Using Data Mining,” in IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2018, doi: 10.1109/ICSCAN.2018.8541242.
• [5] D. Alao and A. B. Adeyemo, “Analyzing Employee Attrition Using Decision Tree Algorithms,” Computing, Information Systems, Development Informatics and Allied Research Journal, vol. 4, no. 1, pp. 17-28, 2013.
• [6] S. O. Abdulsalam, J. F. Ajao, B. F. Balogun, and M. O. Arowolo, “A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms,” 2022.
• [7] X. Gao, J. Wen, and C. Zhang, “An Improved Random Forest Algorithm for Predicting Employee Turnover,” Mathematical Problems in Engineering, vol. 2019, no. 1, 2019.
• [8] S. S. Alduayj and K. Rajpoot, “Predicting Employee Attrition using Machine Learning,” in 2018 International Conference on Innovations in Information Technology (IIT), pp. 93-98.
• [9] X. Gao, J. Wen, and C. Zhang, “An Improved Random Forest Algorithm for Predicting Employee Turnover,” Mathematical Problems in Engineering, vol. 2019, no. 1, 2019.
• [10] S. F. Sari and K. M. Lhaksmana, “Employee Attrition Prediction Using Feature Selection with Information Gain and Random Forest Classification,” Journal of Computer System and Informatics (JoSYC), 2022.
• [11] R. Joseph, S. Udupa, S. Jangale, K. Kotkar, and P. Pawar, “Employee Attrition Using Machine Learning And Depression Analysis,” in 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1000-1005.
• [12] M. M. Alam, K. Mohiuddin, M. K. Islam, M. Hassan, M. A. Hoque, and S. M. Allayear, “A Machine Learning Approach to Analyze and Reduce Features to a Significant Number for Employee’s Turn Over Prediction Model,” Advances in Intelligent Systems and Computing, 2018.
• [13] Y. Chen, X. Lin, and K. Zhan, “Employee Attrition Classification Model Based on Stacking Algorithm,” Psychology Research, vol. 13, no. 6, pp. 279-285, 2023.
• [14] A. Alamsyah and N. Salma, “A Comparative Study of Employee Churn Prediction Model,” in 2018 4th International Conference on Science and Technology (ICST), pp. 1-4.
• [15] S. O. Abdulsalam, J. F. Ajao, B. F. Balogun, and M. O. Arowolo, “A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms,” EAI Endorsed Transactions on Mobile Communications and Applications, vol. 7, no. 21, e4, 2022. doi: 10.4108/eetmca.v6i21.2181.
• [16] D. O. Alao and A. B. Adeyemo, “Analyzing Employee Attrition Using Decision Tree Algorithms,” 2013.
• [17] M. Subhashini and R. Gopinath, “Employee Attrition Prediction in Industry Using Machine Learning Techniques,” International Journal of Advanced Research in Engineering and Technology (IJARET), vol. 11, no. 12, 2020.
• [18] S. S. Reddy, J. Rajanikanth, V. V. Sivaramaraju, and K. V. S. S. R. Murthy, “Prediction of Employee Attrition Using Data Mining,” in 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCA), pp. 1-8.
• [19] A. Qutub, A. Al-Mehmadi, M. Al-Hssan, R. Aljohani, and H. S. Alghamdi, “Prediction of Employee Attrition Using Machine Learning and Ensemble Methods,” International Journal of Machine Learning, vol. 11, no. 2, 2021.
• [20] S. Dutta and S. K. Bandyopadhyay, “Employee Attrition Prediction Using Neural Network Cross Validation Method,” International Journal of Commerce and Management, vol. 6, no. 3, pp. 80-85, 2020.
• [21] S. Agarwal, C. Bhardwaj, G. Gatkamani, R. Gururaj, N. Darapaneni, and A. R. Paduri, “AI Based Employee Attrition Prediction Tool,” Lecture Notes in Computer Science, pp. 580-588, 2023. doi: 10.1007/978-3-031-36402-0_54.
• [22] M. Atef, D. Elzanfaly, and S. Ouf, “Early Prediction of Employee Turnover Using Machine Learning Algorithms,” International Journal of Electrical and Computer Engineering Systems, vol. 13, no. 2, pp. 135-144, 2022.
• [23] T. S. Poornappriya and R. Gopinath, “Employee Attrition In Human Resource Using Machine Learning Techniques,” Webology, vol. 18, no. 6.
• [24] M. A. Abelson, “Examination of Avoidable and Unavoidable Turnover,” Journal of Applied Psychology, vol. 72, no. 3, pp. 382-386, 1987.
• [25] H. G. Heneman, T. Judge, and J. D. Kammeyer-Mueller, Staffing Organizations, 7th ed. Mendota House, 2012.
• [26] J. L. Cotton and J. M. Tuttle, “Employee Turnover: A Meta-Analysis and Review with Implications for Research,” Academy of Management Review, vol. 11, no. 1, 1986.
• [27] J. R. Terborg and T. W. Lee, “A Predictive Study of Organizational Turnover Rates,” Academy of Management Journal, vol. 27, no. 4, pp. 793-810, 1984.
• [28] S. M. Carraher, “Turnover Prediction Using Attitudes Towards Benefits, Pay, and Pay Satisfaction Among Employees and Entrepreneurs in Estonia, Latvia, and Lithuania,” Baltic Journal of Management, vol. 6, no. 1, pp. 25-52, 2011.
• [29] A. Erosa, L. Fuster, and D. Restuccia, “Fertility Decisions and Gender Differences in Labor Turnover, Employment, and Wages,” Review of Economic Dynamics, vol. 5, no. 4, pp. 856-891, 2002.
• [30] E. M. Ineson, E. Benke, and J. László, “Employee Loyalty in Hungarian Hotels,” International Journal of Hospitality Management, vol. 32, pp. 31-39, 2013.
• [31] A. I. Kraut, “Predicting Turnover of Employees from Measured Job Attitudes,” Organizational Behavior and Human Performance, vol. 13, no. 2, pp. 233-243, 1975.
• [32] D. C. Maynard, T. A. Joseph, and A. M. Maynard, “Underemployment, Job Attitudes, and Turnover Intentions,” Journal of Organizational Behavior, vol. 27, no. 4, pp. 509-536, 2006.
• [33] S. Sellgren, G. Ekvall, and G. Tomson, “Nursing Staff Turnover: Does Leadership Matter?”, Leadership in Health Services, vol. 20, no. 3
• [34] D. G. Allen and R. W. Griffeth, "Job embeddedness and turnover intention: A multi-method study," Journal of Applied Psychology, vol. 102, no. 4, pp. 620-633, 2017. doi: 10.1037/apl0000181.
• [35] J. Liu, Z. Wang, and C. Lu, "Employee turnover intention and job satisfaction: The moderating role of psychological empowerment," Frontiers in Psychology, vol. 8, p. 1018, 2017. doi: 10.3389/fpsyg.2017.01018.
• [36] Y. Chang and C. Chen, "The relationship between work-family conflict and turnover intention: A study of healthcare professionals," Journal of Nursing Management, vol. 26, no. 5, pp. 587-596, 2018.
• [37] X. Zhai and S. Xu, "Turnover prediction using machine learning techniques: An empirical study," Journal of Business Research, vol. 120, pp. 19-28, 2020.
• [38] P. Chen and L. Zhang, "The role of employee resilience in mitigating turnover intention: Evidence from the financial sector," International Journal of Human Resource Management, vol. 30, no. 8, pp. 1239-1260, 2019.
• [39] F. C. D. Fisher, "Happiness at work," International Journal of Management Reviews, vol. 12, no. 4, pp. 384–412, 2010.
• [40] B. L. Rich, J. A. LePine, and E. R. Crawford, "Job engagement: Antecedents and effects on job performance," Academy of Management Journal, vol. 53, no. 3, pp. 617–635, 2010.
• [41] M. Tims, A. B. Bakker, and D. Derks, "The impact of job crafting on job demands, job resources, and well-being," Journal of Occupational Health Psychology, vol. 18, no. 2, pp. 230–240, 2013.
• [42] E. E. Kossek and J. S. Michel, "Flexible work schedules," in APA Handbook of Industrial and Organizational Psychology, vol. 1, S. Zedeck, Ed. Washington, DC, USA: American Psychological Association, 2011, pp. 535–572.
• [43] H. K. Şimşek, "Makine Öğrenmesi Dersleri 5b: Random Forest (Regresyon)," Medium, Dec. 12, 2022. [Online]. Available: https://medium.com/data-science-tr/makine-öğrenmesi-dersleri-5a-random-forest-regresyon-2a91715a8b66. [Erişim Tarihi: 05.04.2024].
• [44] E. Güler, “Gradient Boosting Nedir?” Medium, Mar. 14, 2024. [Online]. Available: https://efecanxrd.medium.com/gradient-boosting-nedir-2ba518700777. [Erişim Tarihi: 05.04.2024].
• [45] A. C. Kelle ve H. Yüce, “MQTT Trafiğinde DoS Saldırılarının Makine Öğrenmesi ile Sınıflandırılması ve Modelin SHAP ile Yorumlanması,” J. Mater. Mechat. A, vol. 3, no. 1, pp. 50–62, 2022, doi: 10.55546/jmm.995091.
• [46] Saarela, M., Jauhiainen, S. Comparison of feature importance measures as explanations for classification models. SN Appl. Sci. 3, 272 (2021). https://doi.org/10.1007/s42452-021-04148-9
• [47] Menze, B.H., Kelm, B.M., Masuch, R. et al. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics 10, 213 (2009).
• [48] Kristin K. Nicodemus, Letter to the Editor: On the stability and ranking of predictors from random forest variable importance measures, Briefings in Bioinformatics, Volume 12, Issue 4, 2011, Pages 369–373, https://doi.org/10.1093/bib/bbr016
• [49] Kaggle. HR_comma_sep.csv. https://www.kaggle.com/datasets/liujiaqi/hr-comma-sepcsv. Erişim Tarihi: 04.03.2024