Email this article 
Perbandingan Metode Neural Network Model Radial Basis Function Dan Multilayer Perceptron Untuk Analisa Risiko Kredit Mobil
Abstract
Problems are often encountered in the provision of credit is to determine lending decisions to someone, while other issues are not all credit payments can run well. Among the causes are errors of judgment in making credit decisions. In this study will be used neural network with radial basis function method and neural network with multilayer perceptron method to analyze the risk of car credit, then compare which method is the better. From the test results to measure the performance of the method is to use testing methods confusion matrix and ROC curve, it is known that the method of neural network with multilayer perceptron is better than method of neural network with radial basis function where has a value of accuracy is 96,1% and value of AUC is 0.999. This shows that the model produced, including the classification is Exellent Clasification because it has the value of AUC between 0.90- 1.00.
Full Text:
PDF (Bahasa Indonesia)References
Alpaydin, Ethem. Introduction to Machine Learning. London: The MIT Press., 2010.
Amrin. “Analisa Kelayakan Pemberian Kredit Mobil Dengan Menggunakan Metode Neural Network Model Radial Basis Function.” Jurnal Paradigma AMIK BSI Jakarta (2017): 102-107.
Gorunescu, Florin. Data Mining: Concepts, Models, and Techniques. Verlag Berlin Heidelberg: Springer, 2011.
Han, J., & Kamber, M. Data Mining Concept and Tehniques. San Fransisco: Morgan Kauffman, 2006.
Kothari. Data Mining Concepts and Techniques. San Fransisco: Morgan Kauffman, 2004.
Kusrini, &Luthfi, E. T. Algoritma Data Mining . Yogyakarta: Andi Publishing, 2009.
Kusumadewi, Sri. Pengantar Jaringan Syaraf Tiruan. Yogyakarta: Teknik Informatika FT UII, 2010.
Larose, D. T. Discovering Knowledge in Data. New Jersey: John Willey & Sons, Inc. , 2005.
Purnomo, Mauridhi Hery, & Kurniawan, Agus. Purnomo, MauridhiSupervised Neural Networks dan Aplikasinya. Yogyakarta: Graha Ilmu, 2006.
Puspitaningrum, Diyah. Pengantar Jaringan Syaraf Tiruan. Yogyakarta: Andi Offset, 2006.
Rivai, Veithzal.,&Veithzal, Andria Permata. Credit Management Handbook. Jakarta: Raja Grafindo Persada, 2006.
Santosa, Budi. Data Mining Teknik Pemanfaatan Data Untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu, 2007.
Shukla, A., Tiwari, R., & Kala, R. Real Life Applications of Soft Computing. United States of America : Taylor and Francis Group, LLC., 2010.
Sogala, Satchidananda S. Comparing the Efficacy of the Decision Trees with Logistic Regression for Credit Risk Analysis. India, 2006.
Subekti, Sri Ayu, Lilik Linawati, Adi Setiawan. “Penggunaan Metode Fuzzy Mamdani Dan Sugeno Untuk Pengambilan Keputusan Dalam Analisis Kredit.” Seminar Nasional Matematika Dan Pendidikan Matematika. Yogyakarta: Pendidikan Matematika FMIPA Universitas Negeri Yogyakarta, 2013.
Sumathi, & S., Sivanandam, S.N. Introduction to Data Mining and its Applications. Berlin Heidelberg New York: Springer, 2006.
Sutikno, Tole, Ardi Pujianta, Yuni Tri Supanti. “Prediksi Resiko Kredit Dengan Jaringan Syaraf Tiruan Backpropagation.” Seminar Nasional Aplikasi Teknologi Informasi 2007 (SNATI 2007). Yogyakarta: Universitas Ahmad Dahlan, 2007. 73-76.
Venkatesan, P , & Anitha, S. “Application of a Radial Basis Function Neural Network for Diagnosis of Diabetes Mellitus.” Current Science, Vol. 91, NO.9 (2006): 1195-1199.
Vercellis, Carlo. Business Intelligent: Data Mining and Optimization for Decision Making. Southern Gate, Chichester, West Sussex: John Willey & Sons, Ltd. Southern Gate, Chichester, West Sussex: John Willey & Sons, Ltd.: Vercellis, Carlo (2009). Business Intelligent: Data Mining and OpJohn Willey & Sons, Ltd., 2009.
Witten, I. H., Frank, E., & Hall, M. A. Data Mining: Practical Machine Learning and Tools. Burlington: Morgan Kaufmann Publisher, 2011.
DOI: https://doi.org/10.31294/p.v20i1.2783
Copyright (c) 2018 Amrin Amrin
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
ISSN: 2579-3500
