Analisis Risiko Kredit Perbankan Menggunakan Algoritma K-Nearest Neighbor dan Nearest Weighted K-Nearest Neighbor

Dian Tri Wilujeng, Mohamat Fatekurohman, I Made Tirta

Abstract

Bank is a business entity that collects public funds in the form of savings and also distributes them to the public in the form of credit or other forms.  Credit risk analysis can be done in various ways such as marketing analysis and big data using machine learning.  One example of a machine learning algorithm is K-Nearest Neighbor (KNN) and the development of the K-Nearest Neighbor algorithm is Neighbor Weighted KNearest Neighbor (NWKNN).  The K-Nearest Neighbor (KNN) algorithm is one of the machine learning methods that can be used to facilitate the classification of complex data.  The purpose of this study is to determine the results of the application of the algorithm and the comparison of the use of the KNN and NWKNN algorithms in banking credit.  The results obtained are that NWKNN is able to predict credit risk better, especially in classifying potential customers with potential losses compared to KNN. 

Keywords: Machine learning, KNN, NWKNN

Full Text:

PDF

References

Bradley, A. P, “The Use of the Area Under the Curve in The Evaluation of Machine Learning Algorithms”, Pattern Recognition: Department of Electrical and Computer Engineering, vol. 30, no. 7, pp. 1145-1159, 1997.

Fawcett, T, “An Introduction to ROC Analysis”, Pattern Recognition Letter: Institute for the Study of Learning and Expertise, vol. 27, no. 8, pp. 861–874, 2006.
Gorunescu, F, Data Mining Concepts Model and Techniques, Intelligent Systems Reference Library: Springer-Verlag Berlin Heidelberg, 2011.
Han, J., M. Kamber, dan J. Pei, Data mining: Concepts and Techniques, USA: Elsevier, 2012.
Indriati dan A. Ridok, “Sentiment Analysis for Review Mobile Application Using Neighbor Weighted K-Nearest Neighbor(NWKNN)”, Journal of Enviromental Engineering & Sustainable Technology, vol. 2, no. 19, pp. 23-32, 2016.
Kementerian Sekretariat Negara, Undang-Undang Republik Indonesia Nomor 10 Tahun 1998 Tentang Perubahan atas Undang Undang Nomor 7 Tahun 1992 tentang Perbankan, Jakarta: Kementerian Sekretariat Negara RI.

Krisandi, N., Helmi, dan B. Prihandoso, “Algoritma K-Nearest Neighbor Dalam Klasifikasi Data Hasil Produksi Kelapa Sawit Pada PT. MINAMAS Kecamatan Parindu”, Buletin Ilmiah Math. Stat. dan Terapannya (Bimaster), vol. 2, no. 5, pp. 33-38, 2013.

Nugroho, A., Kusrini dan R. Arief, “Sistem Pendukung Keputusan Kredit Usaha Rakyat PT. Bank Rakyat Indonesia Unit Kaluangkrik Magelang”, Citec Journal, vol. 2, no. 1, pp. 1053-1068, 2015.
Utami, S., M. A. Mukid dan Sugito, “Klasifikasi Kinerja Perusahaan di Indonesia dengan Menggunakan Metode Weighted K Nearest Neighbor (Studi Kasus: 436 Perusahaan Yang Terdaftar Di Bursa Efek Indonesia Tahun 2015)”, Jurnal Gaussian, vol. 6, no. 2, pp. 181-191, 2017.
Vercellis, Carlo, Business Intelligence: Data mining and Optimization for Decision Making, Chichester: John Wiley and Sons, 2009.

DOI: https://doi.org/10.13057/ijas.v5i2.58426

Refbacks

  • There are currently no refbacks.