Metode SF-MEREC-RAFSI dalam Multiple-Criteria Group Decision Making untuk Pendukung Keputusan Penerima Bantuan Keuangan Perbankan
SF-MEREC-RAFSI Method in Multiple-Criteria Group Decision Making for Bank Financial Aid Recipient Selection

Date
2025Author
Irvanizam
Advisor(s)
Mahyuddin
Tulus
Nababan, Erna Budhiarti
Metadata
Show full item recordAbstract
Decades ago, the banking system relied on expert judgment to make financial decisions by qualitatively assessing risks based on company financial reports, business scenarios, and individual client interviews through conventional methods. However, such an approach led to relatively subjective evaluation processes involving more complex criterion conditions with specific requirements and incorporating uncertain information. This condition raised concerns over inconsistency, uncertainty, and indeterminacy in interpreting data, posing significant challenges in decision-making. Moreover, another challenge lies in the lack of resistance to the rank reversal problem, which commonly occurs in evaluation processes. Therefore, this study introduces the SF-MEREC-RAFSI method based on multiple-criteria group decision making (MCGDM) to select a bank financial assistance recipient. The proposed method has two procedures, namely the expert weight calculation procedure using the variability and standard deviation approach, and the criterion weight calculation and alternative selection procedure using the Method Based on the Removal Effects of Criteria-Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval approach. The ranking results of the proposed method demonstrate that they are in line and positively correlated with several other spherical fuzzy decisionmaking methods that are authenticated through comparative and Weighted Rank Spearman's Coefficient Correlation analyses. Additionally, the results of the rank reversal problem analysis exhibit the ability and stability of the proposed framework in dealing with inconsistent, uncertain, and uncertain data, and ensuring its resilience during the evaluation process.