Laboratory Assistant Selection Using Fuzzy BWM and Fuzzy TOPSIS with Sensitivity Analysis and Monte Carlo Simulation
DOI:
https://doi.org/10.52436/1.jutif.2026.7.4.5686Keywords:
Decision Support System, Fuzzy Best–Worst Method, Fuzzy TOPSIS, Monte Carlo Simulation, Sensitivity AnalysisAbstract
This study addresses the challenge of laboratory assistant selection at Universitas Nurdin Hamzah (UNH) Jambi, where manual assessment can introduce subjectivity and limited transparency. To improve objectivity and traceability, this research develops a web-based Decision Support System (DSS) by integrating the Fuzzy Best–Worst Method (Fuzzy-BWM) to derive criteria weights and the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) to rank candidates. The evaluation uses four criteria: Competency Test (C1), Certification (C2), Grade Point Average (GPA) (C3), and Interview (C4), with assessments represented by linguistic fuzzy scales to accommodate uncertainty in human judgment. The system generates ranking outputs along with the Closeness Coefficient (CC) as an interpretable decision indicator. Robustness is further examined using sensitivity analysis and Monte Carlo simulation under varying criteria weights to evaluate ranking stability. Results show that the proposed DSS produces measurable and explainable rankings and provides additional evidence of decision robustness under weight perturbations. From an Informatics and Computer Science perspective, this work demonstrates the practical integration of fuzzy-MCDM algorithms into a reliable computerized DSS that supports transparent, reproducible, and auditable decision-making in academic operational management.
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