Implementation of DSS for the Selection of MSME Financing Based on Financial Performance and Growth Potential - Willy Eka Septian; Maya Permata Sari; Rinaldi Adam; Muhammad Ibrahim; Silvia Maharani; Lena Magdalena (Universitas Bina Sarana Informatika, 2026)
Many businesses have difficulty choosing a funding scheme that suits their financial condition and their company's growth potential. The Analytical Hierarchy Process (AHP) method and the Order of Preference by Similarity to Ideal Solution (TOPSIS) technique are combined to create the right Decision Support System (DSS). This research collected data through surveys and interviews with experts in 30 relevant agency-assisted MSMEs, but the data that will be displayed in this journal are as many as 5 MSME data that we have selected. Then, using AHP to determine how important financial criteria and business prospects are, and using the TOPSIS method to compare alternative financing such as people's business loans, fintech loans, and venture capital. Research outputs include a prototype of a system that can generate scientific publications, policy inputs, and objective financing advice for financial institutions. It is hoped that with this DSS, the financing selection process will be more measurable and accurate, and will support the sustainable growth of MSMEs. This resualt shows that the Analytical Hierarchy Process (AHP) method is able to produce a consistent criterion weight with a Consistency Ratio (CR) value of < 0.1, where business duration (0.22) and income (0.17) are the most dominant factors in assessing the feasibility of MSME financing. Furthermore, the TOPSIS method was used to produce an objective MSME ranking, with Krh Kreasikus Production obtaining the highest preference value (Ci = 0.6911) and Nainay Salted Egg Producer obtaining the lowest value (Ci = 0.2259), demonstrating the ability of this method in distinguishing at-risk MSMEs and MSMEs with better growth potential. Overall, the AHP-TOPSIS-based Decision Support System model has proven to be effective in helping financial institutions evaluate MSMEs in a more systematic, transparent, and data-based manner compared to conventional methods.