TY  - JOUR
T1  - SALDA-ML: Machine Learning Based System Design to Predict Salary In-crease
AU  - Görmez, Yasin
AU  - Arslan, Halil
AU  - Sarı, Suat
AU  - Danış, Mücahid
PY  - 2022
DA  - February
Y2  - 2022
DO  - 10.54569/aair.1029836
JF  - Advances in Artificial Intelligence Research
JO  - Adv. Artif. Intell. Res.
PB  - Osman ÖZKARACA
WT  - DergiPark
SN  - 2757-7422
SP  - 15
EP  - 19
VL  - 2
IS  - 1
LA  - en
AB  - 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.
KW  - Salary Increase
KW  - Machine Learning
KW  - Work Flow
KW  - Personnel Performance Measurement
KW  - System Design
KW  - Software Develop
CR  - 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.
CR  - 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.
CR  - 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.
CR  - ‘Korn Ferry | Organizational Consulting’. https://www.kornferry.com (ac-cessed Mar. 31, 2021).
CR  - K. S. Koong, L. C. Liu, and R. Fowler, ‘Salaries of information technology managers; A trend analysis’, p. 15, 2003.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - Z. Wang, S. Sugaya, and D. P. T. Nguyen, ‘Salary Prediction using Bidirec-tional-GRU-CNN Model’, p. 4, 2019.
CR  - 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.
CR  - 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.
CR  - S. Das, R. Barik, and A. Mukherjee, ‘Salary Prediction Using Regression Techniques’, Social Science Research Network, Rochester, NY, SSRN Scholarly Paper ID 3526707, Jan. 2020. doi: 10.2139/ssrn.3526707.
CR  - L. Li, X. Liu, and Y. Zhou, ‘Prediction of Salary in UK’, p. 5.
CR  - J. Zhang and J. Cheng, ‘Study of Employment Salary Forecast using KNN Algorithm’, Aug. 2019, pp. 166–170. doi: 10.2991/msbda-19.2019.26.
CR  - A. Mobasshera, K. Naher, T. M. Rezoan Tamal, and R. M. Rahman, ‘Sala-ry Increment Model Based on Fuzzy Logic’, in Artificial Intelligence and Algorithms in Intelligent Systems, Cham, 2019, pp. 344–353. doi: 10.1007/978-3-319-91189-2_34.
CR  - P. Viroonluecha and T. Kaewkiriya, ‘Salary Predictor System for Thailand Labour Workforce using Deep Learning’, in 2018 18th International Sym-posium on Communications and Information Technologies (ISCIT), Sep. 2018, pp. 473–478. doi: 10.1109/ISCIT.2018.8587998.
CR  - R. Chiong, Z. Fan, Z. Hu, M. T. P. Adam, B. Lutz, and D. Neumann, ‘A Sen-timent Analysis-based Machine Learning Approach for Financial Market Prediction via News Disclosures’, in Proceedings of the Genetic and Evolu-tionary Computation Conference Companion, New York, NY, USA, 2018, pp. 278–279. doi: 10.1145/3205651.3205682.
CR  - Ankit and N. Saleena, ‘An Ensemble Classification System for Twitter Sen-timent Analysis’, Procedia Comput. Sci., vol. 132, pp. 937–946, Jan. 2018, doi: 10.1016/j.procs.2018.05.109.
CR  - J. E. Dayhoff and J. M. DeLeo, ‘Artificial neural networks’, Cancer, vol. 91, no. 8, pp. 1615–1635, Apr. 2001, doi: 10.1002/1097-0142(20010415)91:8+ <1615::AID-CNCR1175>3.0.CO;2-L.
CR  - G. Schaefer, ‘ACO classification of thermogram symmetry features for breast cancer diagnosis’, Memetic Comput., vol. 6, no. 3, pp. 207–212, Sep. 2014, doi: 10.1007/s12293-014-0135-9.
CR  - N. F. Abubacker, A. Azman, S. Doraisamy, and M. A. A. Murad, ‘An inte-grated method of associative classification and neuro-fuzzy approach for effective mammographic classification’, Neural Comput. Appl., vol. 28, no. 12, pp. 3967–3980, Dec. 2017, doi: 10.1007/s00521-016-2290-z.
CR  - J. de N. Silva, A. O. de C. Filho, A. C. Silva, A. C. de Paiva, and M. Gattass, ‘Automatic Detection of Masses in Mammograms Using Quality Threshold Clustering, Correlogram Function, and SVM’, J. Digit. Imaging, vol. 28, no. 3, pp. 323–337, Jun. 2015, doi: 10.1007/s10278-014-9739-3.
CR  - O. Manor and E. Segal, ‘Predicting Disease Risk Using Bootstrap Ranking and Classification Algorithms’, PLOS Comput. Biol., vol. 9, no. 8, p. e1003200, Aug. 2013, doi: 10.1371/journal.pcbi.1003200.
CR  - C. Kooperberg, M. LeBlanc, and V. Obenchain, ‘Risk prediction using ge-nome-wide association studies’, Genet. Epidemiol., vol. 34, no. 7, pp. 643–652, doi: 10.1002/gepi.20509.
CR  - M.-S. Cheung, M. L. Maguire, T. J. Stevens, and R. W. Broadhurst, ‘DANGLE: A Bayesian inferential method for predicting protein backbone dihedral angles and secondary structure’, J. Magn. Reson., vol. 202, no. 2, pp. 223–233, Feb. 2010, doi: 10.1016/j.jmr.2009.11.008.
UR  - https://doi.org/10.54569/aair.1029836
L1  - https://dergipark.org.tr/en/download/article-file/2104668
ER  -