Implementasi Jaringan Syaraf Tiruan Dengan Multilayer Perceptron Untuk Analisa Pemberian Kredit
DOI:
https://doi.org/10.30865/jurikom.v5i6.1006Abstract
The Problem that is often faced in giving credit is determining the decision to give credit 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 multilayer perceptron method to analyze the feasibility of giving credit. 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 multilayer perceptron has a value of  96,1% accuracy and AUC value of 0.999. This shows that the model produced, including the classification is Exellent Clasification because it has the AUC values between 0.90- 1.00.
References
Alpaydin, E. (2010). Introduction to Machine Learning. London: The MIT Press.
Amrin, A. (2017). Analisa Kelayakan Pemberian Kredit Mobil Dengan Menggunakan Metode Neural Network Model Radial Basis Function. Paradigma, 19(102), 1410–5063. Retrieved from http://ejournal.bsi.ac.id/ejurnal/index.php/paradigma/article/view/2283
Gorunescu, F. (2011). Data Mining: Concepts, Models, and Techniques. Verlag Berlin Heidelberg: Springer.
Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques. Soft Computing (Vol. 54). San Fransisco: Morgan Kauffman. https://doi.org/10.1007/978-3-642-19721-5
Khotari. (2004). Data Mining Concepts and Technique. San Fransisco: Morgan Kauffman.
Kusumadewi, S. (2010). Pengantar Jaringan Syaraf Tiruan. Yogyakarta: Teknik Informatika FT UII.
Larose, D. . (2005). Discovering Knowledge in Data. New Jersey: John Willey & Sons, Inc.
Liao, T. W. (2007). Recent Advances in Data Mining of Enterprise Data: Algorithms and Application. Singapore: World Scientific Publishing.
Maharani, M., Hasibuan, N. A., Silalahi, N., Nasution, S. D., Mesran, M., Suginam, S., … Yuhandri, Y. (2017). IMPLEMENTASI DATA MINING UNTUK PENGATURAN LAYOUT MINIMARKET DENGAN MENERAPKAN ASSOCIATION RULE. Jurnal Riset Komputer (JURIKOM), 4(4), 6–11. Retrieved from https://www.researchgate.net/publication/312495968
Puspitaningrum, D. (2006). Pengantar Jaringan Syaraf Tiruan. Yogyakarta: Andi Offset.
Rivai, V., & Veithzal, A. P. (2006). Credit Management Handbook. Jakarta: Raja Grafindo Persada.
Santosa, B. (2007). Data Mining Teknik Pemanfaatan Data Untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu.
Sogala, S. S. (2006). Comparing the Efficacy of the Decision Trees with Logistic Regression for Credit Risk Analysis. India.
Sumathi, S., & Sivanandam, S. N. (2006). Introduction to Data Mining and its Applications. Berlin Heidelberg New York: Springer.
Vercellis, C. (2009). Business Intelligent: Data Mining and Optimization for Decision Making. Southern Gate, Chichester, West Sussex: John Willey & Sons, Ltd.
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning and Tools. Burlington: Morgan Kaufmann.
Widayu, H., Nasution, S. D., Silalahi, N., & Mesran, M. (2017). DATA MINING UNTUK MEMPREDIKSI JENIS TRANSAKSI NASABAH PADA KOPERASI SIMPAN PINJAM DENGAN ALGORITMA C4.5. MEDIA INFORMATIKA BUDIDARMA, 1(2). Retrieved from http://ejurnal.stmik-budidarma.ac.id/index.php/mib/article/view/323/273
Windarto, A. P. (2017). Implementation of Data Mining on Rice Imports by Major Country of Origin Using Algorithm Using K-Means Clustering Method. International Journal of Artificial Intelligence Research, 1(2), 26. https://doi.org/10.29099/ijair.v1i2.17



