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All Journal Pendas : Jurnah Ilmiah Pendidikan Dasar Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Proceedings of The ICECRS Ulead : Jurnal E-pengabdian Jurnal Masyarakat Indonesia
Pristiwanto
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Languange, Linguistic, Communication & Media Mathematics Other

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OPTIMALISASI PENILAIAN KELAYAKAN KREDIT DENGAN ALGORITMA BACKPROPAGATION Lubis, Mai Sarah Nur; Ramadhani, Putri; Pristiwanto
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 9 No. 04 (2024): Volume 09, Nomor 04, Desember 2024
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v9i04.18159

Abstract

The implementation of the backpropagation algorithm in customer loan eligibility using Artificial Neural Networks (ANN) with the backpropagation method represents an effective approach for evaluating and predicting loan eligibility. This algorithm allows the use of historical data to train a model capable of identifying complex patterns in customer data, including payment behavior, credit history, and other relevant factors. By leveraging this technique, financial institutions can enhance accuracy in assessing credit risk, optimize loan decisions, and reduce the risk of non-performing loans. Loan eligibility assessment is a crucial aspect in the banking industry for minimizing credit risk and increasing profitability. This study proposes a loan eligibility assessment method using the Backpropagation algorithm within Artificial Neural Networks (ANN). The method aims to identify customer eligibility based on historical data and related features such as income, credit history, and age. The research findings indicate that the ANN Backpropagation model can provide accurate predictions of customer loan eligibility with lower error rates compared to traditional methods. These findings suggest that applying the ANN Backpropagation algorithm can enhance the effectiveness of the credit assessment process, minimize credit default risk, and potentially optimize loan decisions in the banking industry.
Co-Authors Arief, Mhd. Abrar Azlan Barus, Apriwati Emy Rosnawati Faisal Ridho, Faisal Furqan Hery Sunandar Lubis, Mai Sarah Nur Matias Julyus Fika Sirait Maulidina Muhammad Amin Muhammad Amin Putri Ramadhani, Putri Saidi Ramadan Siregar Saidi Ramadan Siregar Siagian, Novriadi Antonius Simangunsong, Pandi Barita Nauli Sitorus, Syahrial Sri Budi Purwaningsih, Sri Budi Taufany, Firdaus Enov At Tuti Andriani Zain, Nur Khofifah