KLASIFIKASI PEMINJAMAN NASABAH BANK MENGGUNAKAN METODE NEURAL NETWORK
Payment of loans that experience difficulties in repayment or often called bad credit is a very detrimental thing for the bank, with the occurrence of bad credit the bank does not have the maximum ability to make money for investment. Choosing the right customer must go through the right analysis because the decision to approve or disagree with the loan is the main point that determines the possibility of bad credit. This study aims to classify eligible customers to obtain loans by taking into account existing parameters such as age, total income, number of families, monthly expenditure average, education level and others. This study uses a data mining classification method with a neural network model, to assess the accuracy of data processing using rapid miners then proceed with measurements using confusion matrix, ROC curve. The results of the neural network algorithm after going through confusion matrix testing, the ROC curve shows a very high accuracy value, and the dominant value of AUC and algorithm. The accuracy value is 98.24% with AUC of 0.979
Arun, K., Ishan, G., & Sanmeet, K. (2016). Loan Approval Prediction based on Machine Learning Approach. 18(3), 2016. https://doi.org/10.9790/0661-1803017981
Defu, Z., Stephen, C. H. L., & Zhimei, Y. (2008). A decision tree scoring model based on genetic algorithm and K-means algorithm. Proceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008, 1, 1043–1047. https://doi.org/10.1109/ICCIT.2008.110
Fadly, M., Uddin, N., & Sutarto, H. Y. (2002). Flutter Suppression Using Neural Networks : Design and Implementation. (January 2017).
Hadianto, N., Novitasari, H. B., Rahmawati, A., Prasetyo, R., Miharja, J., & Komputer, I. (2019). Klasifikasi peminjaman nasabah bank menggunakan metode neural network. 14(2), 1–7.
Ir. Adi Sucipto, M. K. (n.d.). CREDIT PREDICTION WITH NEURAL NETWORK ALGORITHM Ir . Adi Sucipto , M . Kom . Sains and Technology Faculty Universitas Islam Nahdlatul Ulama Jepara. (15), 978–979.
Iriadi, N., & Nuraeni, N. (2016). kajian penerapan metode klasifikasi data mining algoritma C4.5 untuk prediksi kelayakan kredit pada bank mayapada jakarta. Jurnal Teknik Komputer AMIK BSI (JTK), 2, 132–137.
Larose, D. T. (2005). Discovering Knowledge in Data. New Jersey: Johny Wiley & Sons, Inc.
Marhumi, S. (2017). Analisis Manajemen Perkreditan Untuk Meningkatkan Profitabilitas Pada Bank Bni Wilayah Vii Makassar. Perspektif, 02(01), 2355–2538. Retrieved from www.journal.unismuh.ac.id/perspektif
Putri, C. B. (2018). Klasifikasi Nasabah Thera Bank Membeli Personal Loan Menggunakan Metode Klasifikasi Dalam Machine Learning.
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002
Sucipto, A. (2012). CREDIT PREDICTION WITH NEURAL NETWORK ALGORITHM Ir . Adi Sucipto , M . Kom . Sains and Technology Faculty Universitas Islam Nahdlatul Ulama Jepara. (15), 978–979.
Wang, Q., Lai, K. K., & Niu, D. (2011). Green credit scoring system and its risk assessemt model with support vector machine. Proceedings - 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011, 284–287. https://doi.org/10.1109/CSO.2011.143
Windarto, A. P. (2017). Implementasi Jst Dalam Menentukan. Sains Komputer & Informatika, 1(1), 12–23.
Yadav, S., & Shukla, S. (2016). Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification. Proceedings - 6th International Advanced Computing Conference, IACC 2016, (Cv), 78–83. https://doi.org/10.1109/IACC.2016.25
Yalidhan, M. D. (2018). Implementasi Algoritma Backpropagation Untuk Memprediksi Kelulusan Mahasiswa. Klik - Kumpulan Jurnal Ilmu Komputer, 5(2), 169. https://doi.org/10.20527/klik.v5i2.152
Abstract viewed = 167 times
PDF downloaded = 344 times
Copyright (c) 2019 Nur Hadianto, Hafifah Bella Novitasari, Ami Rahmawati
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to the PILAR Nusa Mandiri journal as the publisher of the journal, and the author also holds the copyright without restriction.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations. The reproduction of any part of this journal, its storage in databases, and its transmission by any form or media, such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc. , are allowed with written permission from the PILAR Nusa Mandiri journal.
PILAR Nusa Mandiri journal, the Editors and the Advisory International Editorial Board make every effort to ensure that no wrong or misleading data, opinions, or statements be published in the journal. In any way, the contents of the articles and advertisements published in the PILAR Nusa Mandiri journal are the sole and exclusive responsibility of their respective authors and advertisers.