Perbandingan Metode k-Nearest Neighbor, Regresi Logistik Biner, dan Pohon Klasifikasi pada Analisis Kelayakan Pemberian Kredit

Shantika Martha, Wirda Andani, Setyo Wira Rizki

Abstract


Kredit Tanpa Anggunan (KTA) are bank loans given to debtors without asking for guarantees. Some debtors who have made KTA but still need additional loan funds can top up. However, offering this facility to the public cannot be separated from the risk that the debtor and/or other parties fail to fulfill their obligations to the bank. In an effort to assess the feasibility of prospective debtors, banks need decision-making tools so that they can easily and quickly estimate which debtors are able to pay off credit on time (good credit). The tool that is often used is classification. In this study, we will compare 3 classification methods, namely k-nearest neighbor, binary logistic regression, and classification tree, to obtain the best method for analyzing the feasibility of giving KTA top-up. Based on the accuracy value in each method, the classification tree produces the highest accuracy value compared to the other two methods. Thus, for this study, the classification tree is the best method, with an accuracy value of 87.68%. The variables used in the classification tree are DBR, length of service of a debtor, credit limit, type of debtor's occupation, the total income of the debtor, the area where the debtor lives, and the credit period of the debtor is 1 month.

Keywords


Kredit Tanpa Angunan; Classification; k-Nearest Neighbor; Binary Logistic Regression; Classification Tree

Full Text:

PDF

References


Kasmir, Bank dan Lembaga Keuangan Lainnya. Jakarta: PT. Raja Grafindo Persada, 2002.

R. Odegua, "Predicting Bank Loan Default with Extreme Gradient Boosting," arXiv - CS – Statistical Finance; Machine Learning. 2020.

D. A. Salazar, J. L. Velez, J. C. Salazar, "Comparison between svm and logistic regression : which one is better to discriminate?" Revista Colombiana de Estadistica, 35(SPE2), pp. 223-237, June. 2012.

J. Han and M. Kamber, Data Mining Concept and Tehniques. San Fransisco: Morgan Kauffman, 2006.

Y. Maldini, A. M. Siregar dan T. A. Mudzakir, "Perbandingan Algoritma C4.5 dan KNN Untuk Menentukan Pemberian Kredit Bagi Nasabah Koperasi," Scientific Student Journal for Information, Technology and Science., vol. 2, no.1, pp. 31-38, Januari. 2021.

S. Sreesouthry, A. Ayubkhan, M. M. Rizwan, D. Lokesh dan K. P. Raj, "Loan Prediction Using Logistic Regression in Machine Learning," Annals of The Romanian Society for Cell Biology., vol. 25, no. 4, pp. 2790 – 2794, 2021.

M. Madaan, A. Kumar, C. Keshri , R. Jain dan P. Nagrath, "Loan default prediction using decision trees and random forest: A comparative study," IOP Conference Series: Materials Science and Engineering, vol. 1022, no. 1, pp. 012042, 2020.

X. Wu and V. Kumar, The Top Ten Algorithms in Data Mining. New York: CRC Press, 2009.

D. T. Larose, Discovering Knowledge in Data. New Jersey: John Willey & Sons, Inc., 2005.

B. Santoso, Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis 1 edition. Yogyakarta: Graha Ilmu, 2007.

D.W. Hosmer and Lemeshow S, Applied Logistic Regression, Second Edition. New York: John Wiley and Sons, 2002.

A. Agresti, Categorical Data Analysis. New Jersey: John Wiley and Sons, 1990.

R. J. Lewis, "An Introduction to Classification and Regression Trees (CART) Analysis," in The Annual Meeting of the Society for Academic Emergency Medicine, California, UCLA Medical Center, 2000.

L. Breiman, J. Friedman, R. Olshen and C. Stone, Classification and Regression Trees. New York: Chapman Hall, 1993

Bramer and Max, Principles of Data Mining. London: Springer, 2007.




DOI: https://doi.org/10.34312/euler.v10i2.16751

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Shantika Martha, Wirda Andani, Setyo Wira Rizki

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi has been indexed by:


                         EDITORIAL OFFICE OF EULER : JURNAL ILMIAH MATEMATIKA, SAINS, DAN TEKNOLOGI

 Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Negeri Gorontalo
Jl. Prof. Dr. Ing. B. J. Habibie, Tilongkabila, Kabupaten Bone Bolango 96554, Gorontalo, Indonesia
 Email: euler@ung.ac.id
 +62-852-55230451 (Call/SMS/WA)
 Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi (p-ISSN: 2087-9393 | e-ISSN:2776-3706) by Department of Mathematics Universitas Negeri Gorontalo is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.  Powered by Public Knowledge Project OJS.