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Journals / Advances in Artificial Intelligence Research / 2022 / Cilt: 2 - Sayı: 1

SALDA-ML: Machine Learning Based System Design to Predict Salary In-crease

Yasin GÖRMEZ
Halil ARSLAN
Suat SARI
Mücahid DANIŞ
Pages
15–19
Publication Date
2022-02-16
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Specific attributes were determined and a specific scale was developed for performance score for this study.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Number of employees are increases with growing in companies.
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Key Findings & Data:Firms basically make salary increases for their employees in order not to lose their talents and moreover to increase them.
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Conclusion & Impact:Specific attributes were determined and a specific scale was developed for performance score for this study.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Salary Increase · Machine Learning · Work Flow

Abstract

Number of employees are increases with growing in companies. Firms basically make salary increases for their employees in order not to lose their talents and moreover to increase them. Although there is not much problem in how to in-crease the salary in small organizations, this process should be carried out carefully in terms of many parameters in large organizations and should not result in negativities that may disrupt employee motivation. For companies with a large number of employees, creating a model in which the market conditions are determined correctly and all economic parameters are taken into account reveals the need for a process that needs to be worked on for months. In this context, a machine learning-based salary increase prediction system was designed with the study. Specific attributes were determined and a specific scale was developed for performance score for this study.

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Cite This Article

GÖRMEZ, Y., ARSLAN, H., SARI, S., DANIŞ, M. (2022). SALDA-ML: Machine Learning Based System Design to Predict Salary In-crease. Advances in Artificial Intelligence Research, Cilt: 2 - Sayı: 1, 15–19. https://ajindex.com/articles/729000

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References (4)

  1. D. Card, A. Mas, E. Moretti, and E. Saez, ‘Inequality at Work: The Effect of Peer Salaries on Job Satisfaction’, Am. Econ. Rev., vol. 102, no. 6, pp. 2981–3003, Oct. 2012, doi: 10.1257/aer.102.6.2981. R. A. Thacker, ‘Gender, influence tactics, and job characteristics prefer-ences: New insights into salary determination’, Sex Roles, vol. 32, no. 9, pp. 617–638, May 1995, doi: 10.1007/BF01544215. J. R. Hill and N. A. Jolly, ‘Salary Distribution and Collective Bargaining Agreements: A Case Study of the NBA’, Ind. Relat. J. Econ. Soc., vol. 51, no. 2, pp. 342–363, 2012, doi: https://doi.org/10.1111/j.1468-232X.2012.00680.x. ‘Korn Ferry | Organizational Consulting’. https://www.kornferry.com (ac-cessed Mar. 31, 2021).
  2. K. S. Koong, L. C. Liu, and R. Fowler, ‘Salaries of information technology managers; A trend analysis’, p. 15, 2003.
  3. Y. Kathawala, K. J. Moore, and D. Elmuti, ‘Preference between Salary or Job Security Increase’, Int. J. Manpow., vol. 11, no. 7, pp. 25–31, Jan. 1990, doi: 10.1108/01437729010004174. S. Mohan and P. R. Muthuswamy, ‘A study on employee retention in BPO Sector with special reference to Coimbotore’, Int. J. Inf. Futur. Res., vol. 2, no. 6, 2015.
  4. M. Indrasari, B. R. Purnomo, E. Yunus, E. Haryati, and A. R. Hashmi, ‘The Effect of Salary Satisfaction, Work Satisfaction and Organizational Com-mitment To Work Intention’, J. Didask., vol. 1, no. 1, Art. no. 1, Nov. 2018, doi: 10.33856/didaskalia.v1i1.54. P. Khongchai and P. Songmuang, ‘Implement of salary prediction system to improve student motivation using data mining technique’, in 2016 11th International Conference on Knowledge, Information and Creativity Sup-port Systems (KICSS), Nov. 2016, pp. 1–6. doi: 10.1109/KICSS.2016.7951419. Z. Wang, S. Sugaya, and D. P. T. Nguyen, ‘Salary Prediction using Bidirec-tional-GRU-CNN Model’, p. 4, 2019. I. Martín, A. Mariello, R. Battiti, and J. A. Hernández, ‘Salary Prediction in the IT Job Market with Few High-Dimensional Samples: A Spanish Case Study’, Int. J. Comput. Intell. Syst., vol. 11, no. 1, pp. 1192–1209, Jul. 2018, doi: 10.2991/ijcis.11.1.90. U. Bansal, A. Narang, A. Sachdeva, I. Kashyap, and S. P. Panda, ‘Empirical analysis of regression techniques by house price and salary prediction’, IOP Conf. Ser. Mater. Sci. Eng., vol. 1022, p. 012110, Jan. 2021, doi: 10.1088/1757-899X/1022/1/012110. S. Das, R. Barik, and A. Mukherjee, ‘Salary Prediction Using Regression Techniques’, Social Science Research Network, Rochester, NY, SSRN Scholarly Pape