Fuzzy-IOWA-Based Group Decision Support System (GDSS) for Interpreting DASS Scores with Dynamic Expert Weighting - Wiharto; Eka P. Meravigliosi; Umi Salamah; Esti Suryani; Vihi Atina; Pradityo Utomo (Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2026)
This research develops a Group Decision Support System (GDSS) to address subjectivity and ambiguity in the interpretation of Depression Anxiety Stress Scales (DASS-21) scores, particularly in cases of symptom overlap across dimensions. The system introduces a structured weighting mechanism in which the influence of each expert is determined based on objective criteria, including clinical experience, education level, and academic contributions. The methodology applies Simple Additive Weighting (SAW) to quantify the relative importance of five clinical experts, resulting in Expert 2 (27.66%) and Expert 1 (27.36%) having the highest influence within the group. These weights are then incorporated into a Fuzzy-Induced Ordered Weighted Averaging (Fuzzy-IOWA) framework to aggregate expert judgments into a unified consensus model. The results indicate that the proposed approach is able to produce a consistent interpretation structure, with a tendency toward the Anxiety dimension in cases of overlapping symptoms. By integrating expert consensus with patient self-report scores, the system generates a structured interpretation profile of DASS-21 responses. The proposed GDSS provides a systematic and transparent aggregation framework that reflects expert reasoning. However, it is intended as a methodological support tool rather than a substitute for clinical diagnosis. The novelty of this work lies in the integration of SAW-based objective expert capability quantification with Fuzzy-IOWA aggregation and QGDD-based consensus characterization, forming a transparent and mathematically accountable GDSS pipeline that has not been previously applied in the context of DASS-21 interpretation.