Decision Support System For Employee Bonus Allocation At PT. Pesta Pora Abadi Using Multi-Attribute Utility Theory (MAUT)

Abstrak

Employee bonus allocation is a recurring human-resource decision in which multiple performance dimensions must be considered simultaneously. At PT. Pesta Pora Abadi, the existing process relies substantially on manual recapitulation and managerial judgment, creating risks of inconsistent weighting, limited auditability, and avoidable calculation errors. This study develops a desktop-based Decision Support System (DSS) using Multi-Attribute Utility Theory (MAUT) to structure employee bonus decisions across five criteria: discipline, work quality, responsibility, teamwork, and absenteeism. The research combines observation, interviews, literature analysis, requirements engineering, Unified Modeling Language (UML)-based system design, MAUT implementation, and black-box functional testing. MAUT converts heterogeneous criterion values into a common 0–100 utility scale and aggregates them using criterion weights that sum to one. Using an illustrative dataset of five employees, the model produced aggregate utilities of 79.67, 61.46, 61.28, 41.56, and 10.50, with Citra Ayu ranked first. A one-at-a-time sensitivity analysis with ±10% and ±20% perturbations of each criterion weight showed that the first-ranked and last-ranked alternatives remained stable across all scenarios, although the second and third positions were sensitive to modest changes in several weights. The findings indicate that the proposed DSS can make bonus calculations more explicit, reproducible, and traceable while also revealing where managerial weight choices materially affect middle-rank alternatives. The study therefore contributes not only an operational MAUT implementation but also a robustness-oriented interpretation that is relevant to transparent human-resource decision support.