Most organizations use performance appraisal system to evaluate the effectiveness and efficiency of their employees. In evaluating staff performance, it usually involves awarding numerical values or linguistic labels to their performance. These values and labels are used to represent each staff’s achievement by reasoning incorporated in the arithmetical or statistical methods. However, the staff performance appraisal may involve judgments which are based on imprecise data especially when human (the superior) tries to interpret another human (his/her subordinate) performance. Thus, the scores awarded by the appraiser are only approximations. From fuzzy logic perspective, the performance of the appraisee involves the measurement of his/her ability, competence and skills, which are actually fuzzy concepts that can be captured in fuzzy terms. Accordingly, fuzzy approach can be used to handle these imprecision and uncertainty information. Therefore, the performance appraisal system can be examined using Fuzzy Logic Approach and this was carried out in the study. The study utilized hierarchical fuzzy inference approach since performance evaluation comprises of four criteria; namely work achievement, skill knowledge, personal quality, and community services. The output of the study provides the ranking for staff performance. From this study, it is expected that reasoning based on fuzzy models will provide an alternative way in handling various kinds of imprecise data, which often reflected in the way people think and make judgments.


Membership Function Fuzzy Logic Fuzzy System Fuzzy Model Performance Appraisal 


  1. 1.
    Armstrong, M. and Baron, A. (1998). Performance Management: The New Reality, London: Institute of Personnel and DevelopmentGoogle Scholar
  2. 2.
    Schweiger, I. and Sumners, G.E. (1994). Optimizing the Value of Performance AppraisalsGoogle Scholar
  3. 3.
    Chan, D.C.K., Yung, K.L., Ip, A.W.H. (2002). An Application of fuzzy sets to process performance evaluation, Integrated Manufacturing Systems, 237–246.Google Scholar
  4. 4.
    Cordon, O., Herera, F., and Peregrin, A. (1999). Looking for the best Defuzzification method features for each implication operator to design accurate fuzzy model, Department of Computer Science and Artificial Intelligent, Spain.Google Scholar
  5. 5.
    Garibaldi, J.M. and Ifeachor, E.C. (1999). Application of simulated Annealing Fuzzy Model Tuning to Umbilical Cord Acid-base Interpretation, IEEE Transactions on Fuzzy Systems, Vol. 7, No. 1.Google Scholar
  6. 6.
    Hasiloglu, A.S., Yavuz, U., Rezos, S. and Kaya, M.D. (2003). A Fuzzy Expert System for Product Life Cycle Management, International XII, Turkish Symposium on Artificial Intelligence and Neural Networks.Google Scholar
  7. 7.
    Sunghyun, W and Jinil, K. (2001) Learning Achievement Evaluation Strategy Using Fuzzy Membership Function, 31st ASEE/IEEE Frontiers in Education ConferenceGoogle Scholar
  8. 8.
    Tzeng, G.H., Teng, J.Y., Chang, S.L., and Lin, C.W. (2001) Fuzzy Multi-Criteria Evaluation Method for Developmental Strategies Of Hybrid Electric Vehicles, World Energy Council 18th Congress, Buenos Aires.Google Scholar
  9. 9.
    Zimmerman, H.J. (1996). Fuzzy Set Theory and Its Application, Third ed. Kluwer Academic Publishers, Boston, MA.Google Scholar
  10. 10.
    Nasution, H., (2002). Design Methodology of Fuzzy Logic Control, UTMGoogle Scholar

Copyright information

© International Federation for Information Processing 2007

Authors and Affiliations

  • Nureize Arbaiy
    • 1
  • Zurinah Suradi
    • 1
  1. 1.Information System Department, Faculty of Information Technology and MultimediaUniversity of Tun Hussein Onn MalaysiaParit Raja, Batu Pahat, JohorMalaysia

Personalised recommendations