React-Based Static Website Development for Decision Support System Using ROC and SAW: A Case Study on Hedonic Preferences of Chicken Meatballs

Abstract

This study presents the development of Qbico (Q-毘古), a React-based static website decision support system (DSS) integrating the Rank Order Centroid (ROC) method for attribute weighting and the Simple Additive Weighting (SAW) method for alternative ranking. Unlike previous web-based DSS implementations that rely on server-side architectures, Qbico operates entirely on the client-side via React CDN, making it lightweight and freely deployable via GitHub Pages without a dedicated server. The system was evaluated using a case study involving hedonic preference data from four chicken meatball formulations assessed by 18 trained panelists across three attributes: taste, texture, and saltiness level. One-Way ANOVA confirmed statistically significant differences among formulations for all three attributes (p ≤ 0.05). ROC weighting based on expert-determined priority order of taste > texture > saltiness level yielded weights of 0.611, 0.278, and 0.111, respectively. SAW computation produced a final ranking of YA > YB > XA > XB, with formulation YA identified as the best alternative, consistent with manual calculations in Microsoft Excel. Black-box testing across 17 test cases confirmed full functional correctness, and System Usability Scale (SUS) evaluation from 18 respondents yielded an average score of 90.4, corresponding to grade A+ "Best Imaginable," demonstrating excellent usability and user acceptance. Future development may extend Qbico to support additional MADM methods and incorporate data export functionality to broaden its applicability in agroindustrial decision-making.