Penerapan Metode SAW untuk Pemilihan Bank Terbaik Berdasarkan Kriteria Layanan Perbankan bagi Mahasiswa UPN “Veteran” Jakarta

Raisadevi  Ayunda Putri; Nurmalia  Indriyani Putri; Zelda  Meutia Said; Zatin Niqotaini

doi:10.37859/jf.v16i2.11605

Authors

Raisadevi Ayunda Putri UPN "Veteran" Jakarta
Nurmalia Indriyani Putri UPN "Veteran" Jakarta
Zelda Meutia Said UPN "Veteran" Jakarta
Zatin Niqotaini UPN "Veteran" Jakarta

DOI:

https://doi.org/10.37859/jf.v16i2.11605

Keywords: decision support system, SAW method, best bank selection, banking services, UPN “Veteran” Jakarta students

Abstract

Chossing appropriate banking services has become increasingly complex for university students due to the swift expansion of Indonesia's digital financial technology. Previous Decision Support System (DSS) studies in banking, such as those relying on the Analytic Hierarchy Process (AHP), exhibit a critical research gap: they predominantly analyze conventional and digital banks in isolation, use restricted criteria, and neglect the student demographic. To address this gap and establish a distinct novelty, this study introduces an integrated DSS model using the Simple Additive Weighting (SAW) method. Unlike existing AHP-based models that rely heavily on subjective expert judgments, this SAW framework contributes uniquely by combining subjective student preferences with objective institutional metrics, including monthly fees, ATM networks, and branch distributions extracted from official annual reports and corporate websites. The model evaluates both banking sectors across six dimensions tailored for students at UPN "Veteran" Jakarta. Prior to SAW processing, the survey instrument spanning seven faculties was validated via Pearson Correlation and Cronbach's Alpha. The findings reveal that SeaBank achieved the highest preference score (0.878), outperforming BCA (0.794) and BRI (0.786). This demonstrates that while students prioritize administrative cost efficiency and digital agility, security and physical accessibility remain critical. Ultimately, this research advances banking DSS literature by providing a highly objective, student-centric hybrid evaluation system.

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References

Saputri, Y.W., Lestari, A.S. and Hadi, N., 2024. Transformasi Digital dan Antisipasi Perubahan Ekonomi Global dalam Dunia Perbankan. MARAS: Jurnal Penelitian Multidisiplin, 2(1), pp.80-88. doi: https://doi.org/10.60126/maras.v2i1.114.

B. P. S. Indonesia, Jumlah Bank dan Kantor Bank - Tabel Statistik, Badan Pusat Statistik Indonesia. [Online] Available at: https://www.bps.go.id/id/statistics-table/2/OTM3IzI=/jumlah-bank-dan-kantor-bank.html. [Accessed 20 May 2026]

Otoritas Jasa Keuangan and Badan Pusat Statistik, Survei Nasional Literasi dan Inklusi Keuangan (SNLIK) Tahun 2025. Jakarta: Otoritas Jasa Keuangan, 2025.

Budiarti, A.L., Wulandari, E., Rodiah, R.D., Anjani, U.S., Rozak, R.W.A. and Mulyani, H., 2023. Perspektif Mahasiswa Terhadap Transaksi Digital dalam Penggunaan Mobile Banking. Disclosure: Journal of Accounting and Finance, 3(1), pp. 99–117. doi: https://doi.org/10.29240/disclosure.v3i1.6951.

Putri, M.A. and Arfiansyah, M.A., 2025. Faktor yang Mempengaruhi Keputusan Pengguna BSI Mobile. Jurnal Menara Ekonomi: Penelitian dan Kajian Ilmiah Bidang Ekonomi, 11 (1), pp. 51–60. doi: https://doi.org/10.31869/me.v11i1.6539.

Arif, G.Z.A. and Sulaiman, R., 2023. Integrasi Metode Ahp-Topsis Dalam Pemeringkatan Bank Digital Di Indonesia. MATHunesa: Jurnal Ilmiah Matematika, 11(2), pp. 199-208. doi: https://doi.org/10.26740/mathunesa.v11n2.p199-208.

Roy, P. and Shaw, K., 2023. A fuzzy MCDM decision-making model for m-banking evaluations: comparing several m-banking applications. Journal of Ambient Intelligence and Humanized Computing, 14, pp. 11873–11895. doi: https://doi.org/10.1007/s12652-022-03743-x.

Eyceyurt Batir, T. and Babacan, A., 2025. The Relationship between Asset Size and Performance in Islamic Banks: An Application with SWARA-AHP-COPRAS Methods in the Covid-19 Era. Journal of Central Banking Theory and Practice, 14(2), pp. 195–224. doi: https://doi.org/10.2478/jcbtp-2025-0019.

C. Park, M. Son, J. Kim, B. Kim, Y. Ahn, and N. Kwon, “TOPSIS and AHP-Based Multi-Criteria Decision-Making Approach for Evaluating Redevelopment in Old Residential Projects,” Sustainability, vol. 17, no. 15, Aug. 2025, doi: 10.3390/su17157072.

Supriatin, A. and Yana, A.A., 2022. SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN BANK TERBAIK UNTUK PEMBUKAAN REKENING MENGGUNAKAN METODE ANALYTICAL HIERARCHY PROCESS (AHP): Metode Analytical Hierarchy Process. Informatics and Computer Engineering Journal, 2 (1), pp. 39-48. doi: https://doi.org/10.31294/icej.v2i1.627.

S. Hussain, J.-H. Chen, and T. Hussain, “Decision-Making Framework for Improving Bank Performance in Emerging Markets: The Analysis of AHP-TOPSIS and AHP-GRA Models,” Journal of Central Banking Theory and Practice, vol. 13, no. 3, pp. 191–218, Sep. 2024, doi: https://doi.org/10.2478/jcbtp-2024-0027.

Asrulla, A., Risnita, R., Jailani, M.S. and Jeka, F., 2023. Populasi dan Sampling (Kuantitatif), Serta Pemilihan Informan Kunci (Kualitatif) dalam Pendekatan Praktis. Jurnal Pendidikan Tambusai. 7(3), pp. 26320-26332. doi: https://doi.org/10.31004/jptam.v7i3.10836.

Zulfikar, R., Sari, F.P., Fatmayati, A., Wandini, K., Haryati, T., Jumini, S., Nurjanah, Annisa, S., Kusumawardhani, O.B., Mutiah, R., Linggi, A.I. and Fadilah, H. 2024. Metode Penelitian Kuantitatif. Teori, Metode dan Praktik. Widina Media Utama.

Saidah, N., Mawaddah, N., Hidayah, N., Zulpan, Husein, A. and Arianto. 2026. Konsep Reliabilitas dalam Penelitian dan Teknik Pengujianya. Education Journal. 1(3), pp. 775-784. doi: https://doi.org/10.63822/9f7vte41.

Wicaksono, F., Nugroho, F.G., Martinus, A.I. and Tayane, R.T., 2024. SISTEM PENDUKUNG KEPUTUSAN REKOMENDASI KARIR MAHASISWA TEKNIK INFORMATIKA MENGGUNAKAN METODE AHP DAN SAW PADA UNIVERSITAS MUHAMMADIYAH CIREBON. Jurnal Teknologi Informasi (JTI), 12 (2), pp. 33–39. doi: https://doi.org/10.58839/jti.v12i2.1485.