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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal Of Artificial Intelligence And Software Engineering
Sri Kurnia
Program Studi Magister Teknologi Informasi, Universitas Malikussaleh
Computer Science & IT

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Decision Support System for Motorcycle Credit Eligibility Assessment Using the Simple Multi-Attribute Rating Technique Sri Kurnia; Dahlan Abdullah; Nurdin Nurdin
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.8963

Abstract

Sistem Pendukung Keputusan (SPK) merupakan sistem berbasis komputer yang digunakan untuk membantu proses pengambilan keputusan dengan memanfaatkan data, model analisis, dan antarmuka pengguna secara terintegrasi. Penerapan SPK diperlukan dalam proses evaluasi kelayakan kredit sepeda motor agar keputusan yang dihasilkan lebih objektif dan akurat. Pada PT XYZ Kota Lhokseumawe, proses penilaian kelayakan kredit masih dilakukan secara manual sehingga memerlukan waktu yang lama dan berpotensi menimbulkan kesalahan dalam pengambilan keputusan. Penelitian ini bertujuan untuk menerapkan metode SMART (Simple Multi-Attribute Rating Technique) dalam sistem pendukung keputusan untuk menentukan kelayakan kredit sepeda motor. Metode penelitian meliputi pengumpulan data, penentuan kriteria dan bobot, perhitungan nilai utility, serta perhitungan nilai preferensi untuk menentukan tingkat kelayakan calon pelanggan. Hasil penelitian menunjukkan bahwa dari 103 calon pelanggan, sebanyak 45 pelanggan (44%) dinyatakan layak memperoleh kredit dengan nilai preferensi ≥ 0,60, sedangkan 58 pelanggan (56%) dinyatakan tidak layak. Metode SMART mampu membantu perusahaan dalam meningkatkan akurasi evaluasi kredit.
Comparative Analysis of the SMART and ARAS Methods in a Decision Support System for Motorcycle Loan Applicant Eligibility Sri Kurnia; Dahlan Abdullah; Nurdin Nurdin; Munirul Ula; Muchlish Abdul Muthalib
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13714

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.
Co-Authors Dahlan Abdullah Muchlish Abdul Muthalib Munirul Ula Nurdin Nurdin