Comparative Analysis of the SMART and ARAS Methods in a Decision Support System for Motorcycle Loan Applicant Eligibility
DOI:
https://doi.org/10.30871/jaic.v10i4.13714Keywords:
Decision Support System, SMART, ARAS, Motorcycle Financing, Credit Eligibility, Multi-Criteria Decision Making (MCDM)Abstract
Motorcycle financing is one of the most popular consumer financing products in Indonesia. However, the credit eligibility assessment process at PT Mega Central Finance (MCF) Lhokseumawe Branch is still performed manually, making it susceptible to inconsistent evaluations and increasing the risk of non-performing loans. This study aims to implement and compare two Multi-Criteria Decision-Making (MCDM) methods, namely the Simple Multi-Attribute Rating Technique (SMART) and the Additive Ratio Assessment (ARAS), for evaluating motorcycle financing eligibility based on seven criteria, including age, monthly income, occupation, marital status, number of dependents, outstanding debt balance, and down payment (DP). The study utilized primary data from 223 loan applicants collected during the 2024 to 2025 period through interviews and document reviews using a total sampling technique. Criterion weights were determined using expert judgment from credit analysts. The results show that the SMART method classified 96 applicants as eligible and 127 applicants as not eligible, whereas the ARAS method classified 181 applicants as eligible and 42 applicants as not eligible, using a minimum eligibility threshold of 0.60. The difference in the results is primarily attributed to the distinct normalization mechanisms of the two methods. SMART is more sensitive to extreme values in highly weighted criteria, resulting in a more selective evaluation process, whereas ARAS produces a more balanced distribution of preference scores by normalizing criterion values relative to the optimal solution, leading to a more flexible assessment. The findings indicate that the two methods complement each other. SMART is recommended for organizations adopting a conservative credit approval policy, while ARAS is more suitable for organizations seeking to expand the number of eligible applicants while maintaining a balanced consideration of all evaluation criteria.
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