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April Lia Hananto
Buana perjuangan Universitas Karawang, Indonesia

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Prioritizing micro, small, and medium enterprises assistance areas in West Java using analytical hierarchy process Renita Lestari; Baenil Huda; Elfina Novalia; April Lia Hananto
Jurnal Mandiri IT Vol. 14 No. 4 (2026): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i4.527

Abstract

This study aims to develop a Decision Support System (DSS) to prioritize areas for receiving assistance for Micro, Small, and Medium Enterprises (MSMEs) in West Java Province using the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods. The AHP method is used to determine the importance weight of each criterion based on its priority level, while the SAW method is used to carry out the normalization process, calculate preference values, and rank alternative areas. The criteria used include the number of MSMEs, workforce, financial stability ratio, legality ratio, BPP ratio, digital ratio, and innovation ratio. The results of the study indicate that the system built is able to produce an objective and consistent ranking of priority areas for MSME assistance, as evidenced by the agreement between the results of manual calculations using Microsoft Excel and the results of calculations in the system. Thus, this system is expected to assist relevant parties in making decisions regarding the distribution of MSME assistance in a more targeted and structured manner and rank 27 administrative regions in West Java Province. The results show that the highest-ranked region achieved a preference value of 0.8573, indicating its highest priority for MSME assistance, while the lowest-ranked region obtained a value of 0.5129. These results demonstrate the system’s capability to generate consistent and objective rankings. In addition, this study contributes by applying a combined AHP–SAW approach at a regional (macro) level, which is still limited in previous studies, thereby providing a more comprehensive framework for data-driven policy decision-making.