Diterbitkan Oleh:
Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Creation is distributed below Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
KLASIFIKASI PEMINJAMAN NASABAH BANK MENGGUNAKAN METODE NEURAL NETWORK
Keywords:
Loan, Classification, Neural Network, Data Mining, Backpropagation
Abstract
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
Downloads
Download data is not yet available.
References
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
Published
2019-09-05
How to Cite
Hadianto, N., Novitasari, H., & Rahmawati, A. (2019). KLASIFIKASI PEMINJAMAN NASABAH BANK MENGGUNAKAN METODE NEURAL NETWORK. Jurnal Pilar Nusa Mandiri, 15(2), 163-170. https://doi.org/10.33480/pilar.v15i2.658
Section
Articles
Copyright (c) 2019 Nur Hadianto, Hafifah Bella Novitasari, Ami Rahmawati
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
- Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
- Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.
