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All Journal TEKNIK INFORMATIKA Journal of the Indonesian Mathematical Society Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BAREKENG: Jurnal Ilmu Matematika dan Terapan Communication in Biomathematical Sciences Recent in Engineering Science and Technology Lentera Jurnal Pengabdian Maysarakat
Nugraha, Edwin Setiawan
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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A Backpropagation Artificial Neural Network Approach for Loan Status Prediction Nugraha, Edwin Setiawan; Sitepu, Gabrielle Jovanie
JURNAL TEKNIK INFORMATIKA Vol. 15 No. 2 (2022): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v15i2.27006

Abstract

Providing credit has become a main source of profit for financial and non-financial institutions. However, this transaction might lead into credit risk. This risk occurred if debtors unable to complete their obligations that will led loss for creditors.  It is necessity for company to create assessment in distinguishing eligible or non-eligible prospective customer. Artificial Neural Network (ANN) is introduced in solving this typical classification case. Furthermore, one of learning algorithm in ANN namely Backpropagation is able to minimizing error of output in order to receive accurate result. This research aims to form models that capable in classifying the loan status of applicants by utilizing historical data. The method developed in this research is Backpropagation with activation function is a sigmoid function. In addition, this research formed two data model for analyzed; with first data model is every variable given in dataset and for the second data model is the variables that influenced the loan acceptance. Backpropagation shows high performance with more or less data variables. The results of this research show that the both data model has highest accuracy of prediction is 94.37% while the lowest accuracy prediction is 80.28%.
Co-Authors Alika, Zalfani Alvina, Celine Ardiansyah, Trio Azizah, Jasmine Bonyah, Ebenezer Dani Suandi Dedi Rianto Rahadi Dwi Wijonarko Fitriani agustina Hamzah, Dadang Amir Ilahi, Fadilah Karunia Eka Lestari Khaerunnisa, Ine Mokhammad Ridwan Yudhanegara Muhammad Faisal Olivia, Agna Pratama, Muhammad Nabil Priambodo, Oktanindita Ramadhan, Joko Iwan Ramdhani, Randi Risnawita Risnawita, Risnawita Samsul Arifin Sisilia Sylviani Sitepu, Gabrielle Jovanie Sudding, Fauziah Nur Fahirah Zalukhu, Jernihlawati