Web-based SMART method decision support system for assessing eligibility for small and medium enterprises (SMEs) loan assistance
The process of determining eligibility for getting capital loan assistance for small and medium-sized firms (SMEs) is critical in making financial decisions.However, many manual approaches are subjective and less structured, resulting in a greater chance of error.This research responds to the challenges of assessing and selecting eligible small and medium enterprises to receive capital loan assistance.To analyze how well the business satisfies the eligibility requirements, this data will be evaluated using SMART criteria.Following the evaluation, the system will produce a feasibility score that may be utilized as a decision-making tool.The results of the test indicate that, in comparison to the manual approach, the system is able to provide a more structured and objective assessment.Based on SMART criteria, the resulting feasibility score paints a clear picture of the likelihood of company success.The main conclusion to be drawn from this study is that linked parties can use a method that is more accurate and efficient to help them decide whether to accept capital loan assistance.The SMART method's application results in a more comprehensive and quantifiable evaluation.To ensure that the system can accommodate variances in various company kinds, it is vital to be aware of the constraints while choosing the criteria and weights employed.This Decision Support System combines the SMART technique with web-based technology to provide a more systematic approach to evaluate the possibility of acquiring small and medium firm financial assistance.It is intended that by offering more objective rules, this method will aid in better decision making and help the development of small and medium-sized businesses.
Authors
- Dadan Ramdani Nugraha (ORCID: https://orcid.org/0000-0002-4707-7852)
- Nunu Nugraha (ORCID: https://orcid.org/0000-0003-0208-4810)
- Raina Raina
Institutions
- Universitas Kuningan (ID)
Publication Details
- Journal
- AIP conference proceedings
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1063/5.0273153
- Primary Topic
- Advanced Data Processing Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00