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Reseach Article

Modeling Performance Appraisal using Soft Computing and ANN

by Sanket Ghorpade, J. V. Shinde
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 8
Year of Publication: 2016
Authors: Sanket Ghorpade, J. V. Shinde

Sanket Ghorpade, J. V. Shinde . Modeling Performance Appraisal using Soft Computing and ANN. International Journal of Computer Applications. 146, 8 ( Jul 2016), 18-23. DOI=10.5120/ijca2016910831

@article{ 10.5120/ijca2016910831,
author = { Sanket Ghorpade, J. V. Shinde },
title = { Modeling Performance Appraisal using Soft Computing and ANN },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 8 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 18-23 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016910831 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:50:39.431961+05:30
%A Sanket Ghorpade
%A J. V. Shinde
%T Modeling Performance Appraisal using Soft Computing and ANN
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 8
%P 18-23
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Evaluation of the quality of an individual's performance in an organization is modeled using a formal management system known as Performance. Performance Appraisal is based on quantitative as well as qualitative parameters. Adherence to performance evaluation parameters such as a specific work schedule, interpersonal skills and innovation, communication skills and team collaboration that is ability to coordinate well with other associates or employees could be some of the factors or performance measures that govern the performance appraisal result. Evaluating some of the factors involve vagueness, uncertainty and imprecision as they are based on judgment making ability of the reviewer. A timesheet can be termed as a process or method for recording the amount of a time utilized by the employee on each job. If multiple such timesheet are integrated, then some of the evaluation parameters could be calculated using soft computing techniques and which help in decision making from available data and experience to provide unbiased decision in performance appraisal. This paper proposes a technique of reducing the vagueness, uncertainty and imprecision by collecting the precise data through the integration of timesheet for an individual. The paper describes the performance evaluation using the proposed system for an individual of an IT organization, considering the vertical as AMS (Application Management Service).

  1. Suzana Demyen, Ion Lala Popa, “Methods of determining the level of performance achieved by human resources in small and medium sized enterprises, using the analysis of specific indicators”, Procedia - Social and Behavioral Sciences, pp. 43-50, 2014.
  2. A. Iyer, “Employee Evaluation Criteria”, URL: teria.html, Retrieved on 29th Sep 2012
  3. V. Nagadevara, V. Srinivasan, & R. Valk, “Establishing a Link between Employee Turnover and Withdrawal Behaviours: Application of Data Mining Techniques”, Research and Practice in Human Resource Management, vol.16, no.1, pp.81-99, 2008.
  4. Nisha Macwan, Priti S. Sajja, (2014), “A Linguistic Fuzzy Approach for Employee Evaluation”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, no. 1, pp.975-980, January 2014.
  5. Ingrid Cliff, “How to conduct an Employee Appraisal: What criteria do you use to measure employee performance?”, 2008.
  6. C.C. Yee, Y.Y.Chen, “Performance Appraisal System using Multifactorial Evaluation Model”, World Academy of Science, Engineering and Technology 53, pp. 231-235, 2009.
  7. Nisha Macwan, Priti S. Sajja, (2013), “Performance Appraisal using Fuzzy Evaluation Methodology”, International Journal of Engineering and Innovative Technology, Volume 3, no. 3, pp.324-329, September 2013.
  8. Ming-Shin Kuo, Gin-Shuh Liang, “A Soft Computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers”, Applied Soft Computing, vol. 12, no. 1, pp.476-485, 2012.
  9. G Meenakshi, “Multi source feedback based performance appraisal system using Fuzzy logic decision support system”, International Journal on Soft Computing ( IJSC ), vol.3, no.1, pp. 91-106, Feb. 2012.
  10. A. Golec, E. Kahya, “A fuzzy model for competency based employee evaluation and selection”, Computers and Industrial Engineering, vol. 3, no.1,, pp. 143-161.
  11. Beer M, Spector B, Lawrence P, Mills DQ, Walton R, (1985), Human Resource Management: a general Managers Perspective, New York.
  12. Caldwell Cam, Truong Do, Linh Pham, Tuan Anh, (2011) Strategic Human Resource Management as Ethical Stewardship, Journal of Business Ethics, 98:171-182.
  13. Ab. Aziz Yusof, Performance Appraisal Issues, Challenges & Prospects, Pearson, 2003.
  14. G. Dessler, Human Resource Management, New Jersey, Pearson Education, Inc., 2000
  15. Guest David, Woodrow Christopher, (2012) Exploring the Boundaries of Human Resource Managers’ Responsibilities, Journal of Business Ethics, 109-119
  16. A. Voloshyn, G. Gnatienko, E. Drobot, “Fuzzy membership functions in a fuzzy decision making problem”, International Journal "Information Theories & Applications" Vol.10, 2003.
  17. ZHU Zhi-hong, XUE Da-wei. Fuzzy evaluation model of accounting firm knowledge management performance. Innovation Management and Industrial Engineering; 2009
  18. Nisha Macwan, Priti S. Sajja, (2013), "Modeling Performance Appraisal using Soft Computing Techniques: Designing Neuro-Fuzzy Application", International Conference on Intelligent Systems and Signal Processing (ISSP), pp.403--407, 2013.
Index Terms

Computer Science
Information Sciences


Performance Appraisal KRA Timesheet Performance Measures KRA rules set Weighted Rating Artificial Neural Network.