Model Hybrid Fuzzy-Weighted Product Evaluasi Kinerja Honorer
DOI:
https://doi.org/10.59435/jocstec.v4i2.726Keywords:
logika fuzzy, pegawai honorer, pengambilan keputusan, weighted product, EvaluasiAbstract
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.
Downloads
References
[1] H. Aguinis, Performance Management, 4th ed. Chicago, IL, USA: Chicago Business Press,
2019.
[2] A. S. DeNisi and K. R. Murphy, "Performance appraisal and performance management: 100
years of progress?," Journal of Applied Psychology, vol. 102, no. 3, pp. 421–433, 2017, doi:
10.1037/apl0000085.
[3] L. A. Zadeh, "Fuzzy Sets," Information and Control, vol. 8, no. 3, pp. 338–353, 1965, doi:
10.1016/S0019-9958(65)90241-X.
[4] A. Mardani, A. Jusoh, K. M. Nor, Z. Khalifah, N. Zakwan, and A. Valipour, "Multiple criteria
decision-making techniques and their applications - A review of the literature from 2000 to
2014," Economic Research-Ekonomska Istraživanja, vol. 28, no. 1, pp. 516–571, 2015, doi:
10.1080/1331677X.2015.1075139.
[5] J. Hutahaean, “Konsep Sistem Informasi”. Deepublish, 2015.
[6] E. Triantaphyllou, Multi-Criteria Decision Making Methods: A Comparative Study. Dordrecht,
Netherlands: Springer, 2000, doi: 10.1007/978-1-4757-3157-6.
[7] A. Mardani, E. K. Zavadskas, Z. Khalifah, A. Jusoh, K. M. Nor, and N. Khoshnoudi, "A review
of fuzzy multiple criteria decision-making applications and challenges," Expert Systems with
Applications, vol. 42, no. 8, pp. 4126-4148, 2015, doi: 10.1016/j.eswa.2015.01.003.
[8] W. Ma, Y. Zhang, and X. Li, "Selection of multi-criteria decision-making methods under
different decision environments," Ecological Indicators, vol. 129, Art. no. 107889, 2021.
[9] S. Greco, M. Ehrgott, and J. R. Figueira, Multiple Criteria Decision Analysis: State of the Art
Surveys, 2nd ed. New York, NY, USA: Springer, 2016.
[10] F. Herrera and E. Herrera-Viedma, "Linguistic decision analysis: Steps for solving decision
problems under linguistic information," Information Fusion, vol. 14, no. 4, pp. 387–396, 2013.
[11] H.-J. Zimmermann, Fuzzy Set Theory and Its Applications, 4th ed. Boston, MA, USA:
Springer, 2001.
[12] C.-T. Chen, "Extensions of the TOPSIS for group decision-making under fuzzy environment,"
Fuzzy Sets and Systems, vol. 114, no. 1, pp. 1–9, 2000.
[13] E. K. Zavadskas, Z. Turskis, and J. Antucheviciene, "Selecting a contractor by using a novel
method for multiple attribute analysis: Weighted Aggregated Sum Product Assessment
(WASPAS)," Studies in Informatics and Control, vol. 21, no. 3, pp. 247–258, 2012.
[14] J. R. Wang, H. Zhang, and Y. Liu, "Hybrid multi-criteria decision-making optimization
models: A review," Knowledge-Based Systems, vol. 245, Art. no. 108625, 2022.
[15] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic
algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–
197, 2002, doi: 10.1109/4235.996017.
[16] Z. Wang, Y. Li, and J. Chen, "Fuzzy decision models in human resource management: A
review," Applied Soft Computing, vol. 108, Art. no. 107456, 2021.
[17] E. K. Zavadskas, J. Antucheviciene, and Z. Turskis, "Multi-criteria decision-making (MCDM)
methods in economics: An overview," Technological and Economic Development of
Economy, vol. 25, no. 1, pp. 1–29, 2019.
[18] J. H. Marler and J. W. Boudreau, "An evidence-based review of HR Analytics," Human
Resource Management Review, vol. 27, no. 1, pp. 3–18, 2017, doi:
10.1016/j.hrmr.2016.03.002.
[19] D. J. Power, Decision Support, Analytics, and Business Intelligence, 3rd ed. New York, NY,
USA: Business Expert Press, 2015.
[20] B. Marr, Artificial Intelligence in Practice. Hoboken, NJ, USA: Wiley, 2019.
[21] S. Barocas, M. Hardt, and A. Narayanan, Fairness and Machine Learning: Limitations and
Opportunities. Cambridge, MA, USA: MIT Press, 2023.
[22] S. Barocas and A. D. Selbst, "Big Data's Disparate Impact," California Law Review, vol. 104,
no. 3, pp. 671–732, 2016.
[23] M. J. Kusner, J. Loftus, C. Russell, and R. Silva, "Counterfactual Fairness," in Advances in
Neural Information Processing Systems (NeurIPS), 2017.
Downloads
Published
License
Copyright (c) 2026 Lia Umbari Putri (Author); Rolly Yesputra (Co Author); Jeperson Hutahaean (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
