Dalam menganalisis kredit
kadang-kadang petugas kredit kurang akurat dalam analisis kredit, sehingga
dapat menyebabkan peningkatan kredit macet . Klasifikasi algoritma data mining
secara luas digunakan untuk menentukan kelayakan kredit dari salah satu Naif
Bayes classifier , NBC unggul dalam meningkatkan nilai akurasi yang tinggi
tetapi lemah dalam pemilihan atribut. Setelah menguji algoritma Naive Bayes menghasilkan
akurasi 89,33 % dan nilai AUC 0.955
Jurnal Teknik Komputer Kelayakan Kredit
Xhemali, D., Hinde, C.J. and Stone. R.G. (2009). Naive Bayes vs Decision Trees vs. Neural Network in the Classification of Training Web Pages. IJCSI International Journal of Computer Science Issues, 4(1). Pp. 16-23.
Yap, Bee W., Ong, Seng H., and Husain. N.H.M, (2011). Using Data Mining to Improve Assessment of Credit Worthiness via Credit Scoring Models. Expert System with Applications, 38(2011) 13274-13283
Undang-Undang Republik Indonesia Nomor 10 Tahun 1998 tentang perubahan atas Undang-Undang Nomor 7 Tahun 1992 tentang perbankan.
Larose, D.T.( 2005). Discovering Knowledge in Data. Canada: Wiley-Interscience.
Larose, D.T.( 2005). Discovering Knowledge in Data. Canada: Wiley-Interscience.
Xhemali, D., Hinde, C.J. and Stone. R.G. (2009). Naive Bayes vs Decision Trees vs. Neural Network in the Classification of Training Web Pages. IJCSI International Journal of Computer Science Issues, 4(1). Pp. 16-23.
Han, J., and Kamber, M. (2006). Data Mining Concept and Techniques. San Francisco: Diane Cerra.
Bramer, Max. (2007). Principles of Data Mining. London: Springer. ISBN-10: 1-84628-765-0, ISBN-13: 978-1-84628-765-7
Gorunescu, Florin. (2011). Data Mining Concepts, Models and Techniques. Intelligent System Reference Library, Vol 12, ISBN 978-3-642-19721-5.
Han, J., and Kamber, M. (2006). Data Mining Concept and Techniques. San Francisco: Diane Cerra.
Vercellis, Carlo. (2009). Business Intelligence: Data Mining and Optimization for Decision Making. United Kingdom: John Willey & Son