Decision Support System Vol. 4 No. 2 (2026): JOCSTEC - Mei
Published: 28-05-2026

Model Hybrid Fuzzy-Weighted Product Evaluasi Kinerja Honorer

Lia Umbari Putri
Akademi Manajemen Informatika dan Komputer Polibisnis
Indonesia
Author
Rolly Yesputra
Universitas Royal
Indonesia
Co Author
Jeperson Hutahaean
Universitas Royal
Indonesia
Author
109 views 113 downloads

Abstract

Evaluasi kinerja pegawai honorer di instansi publik seringkali bergantung pada penilaian linguistik yang subjektif, sehingga memicu bias penilai dan keterbatasan akuntabilitas. Untuk mengatasi masalah tersebut, penelitian ini mengusulkan model hibrida Fuzzy-Weighted Product. Logika Fuzzy diterapkan untuk mentransformasikan istilah linguistik menjadi Triangular Fuzzy Numbers (TFN) dan skor tegas (crisp), sementara metode Weighted Product digunakan untuk mengagregasikan skor tersebut berdasarkan bobot multi-kriteria. Model ini dievaluasi melalui studi kasus yang melibatkan sepuluh pegawai honorer berdasarkan lima kriteria: disiplin, tanggung jawab, kualitas kerja, kerja sama, dan inisiatif. Hasil eksperimen menunjukkan bahwa model hibrida ini berhasil meminimalkan subjektivitas penilai dan menghasilkan perengkingan yang dapat direproduksi secara matematis. Analisis sensitivitas mengonfirmasi stabilitas hasil peringkat akhir, sehingga model Hibrida Fuzzy Weighted Product yang diusulkan ini sangat sesuai untuk digunakan sebagai kerangka kerja utama dalam sistem pendukung keputusan untuk penilaian kinerja di sektor publik.  

Performance evaluation of honorary employees in public institutions often relies on subjective linguistic assessments, leading to evaluator bias and limited accountability. To address this, this paper proposes a Hybrid Fuzzy-Weighted Product (Fuzzy-Weighted Product) model. Fuzzy Logic is adopted to transform linguistic terms into Triangular Fuzzy Numbers (TFN) and crisp scores, while the Weighted Product method aggregates these scores based on multi-criteria weights. The model was evaluated using a case study of ten honorary employees across five criteria: discipline, responsibility, work quality, cooperation, and initiative. The experimental results demonstrate that the hybrid model successfully minimizes evaluator subjectivity and delivers mathematically reproducible rankings. A sensitivity analysis confirms the stability of the final rankings, making the proposed Hybrid Fuzzy–Weighted Product model highly suitable as a core framework for decision-support systems in public sector performance appraisal.

Keywords

logika fuzzy pegawai honorer pengambilan keputusan weighted product Evaluasi

Views & Downloads Statistics (Last 12 Months)

Monthly performance and trends of article views and downloads
views: 109 downloads: 113

References

52 References
Bibliography and cited literature of the article
  1. [1]
    H. Aguinis, Performance Management, 4th ed. Chicago, IL, USA: Chicago Business Press,
  2. [2]
    2019.
  3. [3]
    A. S. DeNisi and K. R. Murphy, "Performance appraisal and performance management: 100
  4. [4]
    years of progress?," Journal of Applied Psychology, vol. 102, no. 3, pp. 421–433, 2017, doi:
  5. [5]
    1037/apl0000085.
  6. [6]
    L. A. Zadeh, "Fuzzy Sets," Information and Control, vol. 8, no. 3, pp. 338–353, 1965, doi:
  7. [7]
    1016/S0019-9958(65)90241-X.
  8. [8]
    A. Mardani, A. Jusoh, K. M. Nor, Z. Khalifah, N. Zakwan, and A. Valipour, "Multiple criteria
  9. [9]
    decision-making techniques and their applications - A review of the literature from 2000 to
  10. [10]
    2014," Economic Research-Ekonomska Istraživanja, vol. 28, no. 1, pp. 516–571, 2015, doi:
  11. [11]
    1080/1331677X.2015.1075139.
  12. [12]
    J. Hutahaean, “Konsep Sistem Informasi”. Deepublish, 2015.
  13. [13]
    E. Triantaphyllou, Multi-Criteria Decision Making Methods: A Comparative Study. Dordrecht,
  14. [14]
    Netherlands: Springer, 2000, doi:10.1007/978-1-4757-3157-6.
  15. [15]
    A. Mardani, E. K. Zavadskas, Z. Khalifah, A. Jusoh, K. M. Nor, and N. Khoshnoudi, "A review
  16. [16]
    of fuzzy multiple criteria decision-making applications and challenges," Expert Systems with
  17. [17]
    Applications, vol. 42, no. 8, pp. 4126-4148, 2015, doi:10.1016/j.eswa.2015.01.003.
  18. [18]
    W. Ma, Y. Zhang, and X. Li, "Selection of multi-criteria decision-making methods under
  19. [19]
    different decision environments," Ecological Indicators, vol. 129, Art. no. 107889, 2021.
  20. [20]
    S. Greco, M. Ehrgott, and J. R. Figueira, Multiple Criteria Decision Analysis: State of the Art
  21. [21]
    Surveys, 2nd ed. New York, NY, USA: Springer, 2016.
  22. [22]
    F. Herrera and E. Herrera-Viedma, "Linguistic decision analysis: Steps for solving decision
  23. [23]
    problems under linguistic information," Information Fusion, vol. 14, no. 4, pp. 387–396, 2013.
  24. [24]
    H.-J. Zimmermann, Fuzzy Set Theory and Its Applications, 4th ed. Boston, MA, USA:
  25. [25]
    Springer, 2001.
  26. [26]
    C.-T. Chen, "Extensions of the TOPSIS for group decision-making under fuzzy environment,"
  27. [27]
    Fuzzy Sets and Systems, vol. 114, no. 1, pp. 1–9, 2000.
  28. [28]
    E. K. Zavadskas, Z. Turskis, and J. Antucheviciene, "Selecting a contractor by using a novel
  29. [29]
    method for multiple attribute analysis: Weighted Aggregated Sum Product Assessment
  30. [30]
    (WASPAS)," Studies in Informatics and Control, vol. 21, no. 3, pp. 247–258, 2012.
  31. [31]
    J. R. Wang, H. Zhang, and Y. Liu, "Hybrid multi-criteria decision-making optimization
  32. [32]
    models: A review," Knowledge-Based Systems, vol. 245, Art. no. 108625, 2022.
  33. [33]
    K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic
  34. [34]
    algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–
  35. [35]
  36. [36]
    Z. Wang, Y. Li, and J. Chen, "Fuzzy decision models in human resource management: A
  37. [37]
    review," Applied Soft Computing, vol. 108, Art. no. 107456, 2021.
  38. [38]
    E. K. Zavadskas, J. Antucheviciene, and Z. Turskis, "Multi-criteria decision-making (MCDM)
  39. [39]
    methods in economics: An overview," Technological and Economic Development of
  40. [40]
    Economy, vol. 25, no. 1, pp. 1–29, 2019.
  41. [41]
    J. H. Marler and J. W. Boudreau, "An evidence-based review of HR Analytics," Human
  42. [42]
    Resource Management Review, vol. 27, no. 1, pp. 3–18, 2017, doi:
  43. [43]
    1016/j.hrmr.2016.03.002.
  44. [44]
    D. J. Power, Decision Support, Analytics, and Business Intelligence, 3rd ed. New York, NY,
  45. [45]
    USA: Business Expert Press, 2015.
  46. [46]
    B. Marr, Artificial Intelligence in Practice. Hoboken, NJ, USA: Wiley, 2019.
  47. [47]
    S. Barocas, M. Hardt, and A. Narayanan, Fairness and Machine Learning: Limitations and
  48. [48]
    Opportunities. Cambridge, MA, USA: MIT Press, 2023.
  49. [49]
    S. Barocas and A. D. Selbst, "Big Data's Disparate Impact," California Law Review, vol. 104,
  50. [50]
    no. 3, pp. 671–732, 2016.
  51. [51]
    M. J. Kusner, J. Loftus, C. Russell, and R. Silva, "Counterfactual Fairness," in Advances in
  52. [52]
    Neural Information Processing Systems (NeurIPS), 2017.

Article Metrics

109
views
113
downloads
Dimensions Citation Badge

Issue

JOCSTEC - Mei

Vol. 4 No. 2 (2026)
Section: Decision Support System
pp. 60-69

How to Cite

[1]
Lia Umbari Putri, Rolly Yesputra, and Jeperson Hutahaean, Trans., “Model Hybrid Fuzzy-Weighted Product Evaluasi Kinerja Honorer”, Journal Of Computer Science And Technology, vol. 4, no. 2, pp. 60–69, May 2026, doi: 10.59435/jocstec.v4i2.726.

License

Share This Article