Implementation and verification of a SAW-based decision support system for culinary MSME ranking
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Abstract
This study aims to develop a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to determine the most preferred culinary Micro, Small, and Medium Enterprises (MSMEs) in the Dracik Campus area of Batang. The system incorporates four primary criteria—price, location, service, and food quality—each supported by structured sub-criteria and weights derived from customer assessments. Data were collected via questionnaires administered to MSME customers, and the results were normalized and weighted to generate objective rankings. The system architecture was developed using the SDLC model, with MySQL utilized for database management and automated SAW computation. The results indicate that the system successfully generated rankings, and the preference values produced by the automated SAW computation were identical to those from manual SAW calculations, with no numerical deviation, proving the algorithmic correctness of the system. The study confirms that DSS with SAW enhances transparency, reduces subjective bias, and provides actionable insights for MSME development. Overall, this study validates the successful functional implementation of a SAW-based DSS and demonstrates that the system can be used as a reliable computational tool for multi-criteria decision-making.
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