Implementation of SAW in Decision Making for Aid Recipients of MSMEs in Sigi Regency

Junaidi, Fadjryani, Iman Setiawan, Mohammad Fajri, Hartayuni Sain, Veren Graciela Todengko, Syaiful Hendra

Abstract

This study aims to develop a data-driven framework for prioritizing Micro, Small, and Medium Enterprises (MSMEs) assistance in Sigi Regency using the Simple Additive Weighting (SAW) method. The SAW method systematically ranks MSME recipients based on predefined criteria, ensuring a fair and transparent selection process. A web-based Decision Support System (DSS) was developed to implement this method, enabling efficient data processing and large-scale information management. The study collected primary data on business type, initial capital, turnover, and location, which were analyzed using the SAW method. The results identified Laila Khaliq as the top-ranked recipient with a score of 0.85, followed by Nur Ifa (0.82), Supina (0.73), Annur Melati (0.715), and Moh. Randi (0.71), Kaharudin (0.7), and Jhon Laips (0.277). These findings provide a structured approach for government agencies and financial institutions to allocate MSME assistance more effectively.
The novelty of this research lies in integrating the SAW method with a web-based DSS, thereby enhancing the decision-making accuracy and scalability. Unlike conventional subjective selection methods, this approach introduces a systematic, data-driven ranking system that can be expanded to other regions or adapted with alternative decision-making models, thus improving MSME assistance programs.

 

Keywords: SAW, Sigi Regency, MSMEs, Web System.

 

https://doi.org/10.55463/issn.1674-2974.52.2.5


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