Feasibility study for banking loan using association rule mining classifier

(1) * Agus Sasmito Aribowo Mail (Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia)
(2) Nur Heri Cahyana Mail (Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia)
*corresponding author


The problem of bad loans in the koperasi can be reduced if the koperasi can detect whether member can complete the mortgage debt or decline. The method used for identify characteristic patterns of prospective lenders in this study, called Association Rule Mining Classifier. Pattern of credit member will be converted into knowledge and used to classify other creditors. Classification process would separate creditors into two groups: good credit and bad credit groups. Research using prototyping for implementing the design into an application using programming language and development tool. The process of association rule mining using Weighted Itemset Tidset (WIT)–tree methods. The results shown that the method can predict the prospective customer credit. Training data set using 120 customers who already know their credit history. Data test used 61 customers who apply for credit. The results concluded that 42 customers will be paying off their loans and 19 clients are decline


Association Rule Mining Classifier; WIT-Tree; Banking Loan




Article metrics

Abstract views : 1586 | PDF views : 591




Full Text



S. Sadatrasou, M. Gholamian, and K. Shahanaghi, “An application of data mining classification and bi-level programming for optimal credit allocation,” Decis. Sci. Lett., vol. 4, no. 1, pp. 35–50, 2015.

A. H. Kusuma, “Penerapan Data Mining dalam Klasifikasi Nasabah Bank dan Analisis Resiko Persetujuan Proposal Kredit,” Yogyakarta, 2008.

X. Liu and X. Zhu, “Study on the Evaluation System of Individual Credit Risk in commercial banks based on data mining,” in Communication Systems, Networks and Applications (ICCSNA), 2010 Second International Conference on, 2010, vol. 2, pp. 308–311.

Y. Bee Wah and I. R. Ibrahim, “Using data mining predictive models to classify credit card applicants,” in Advanced Information Management and Service (IMS), 2010 6th International Conference on, 2010, pp. 394–398.

M. Mittal, S. Pareek, and R. Agarwal, “Loss profit estimation using association rule mining with clustering,” Manag. Sci. Lett., vol. 5, no. 2, pp. 167–174, 2015.

N. H. Cahyana and A. S. Aribowo, “Decision Support System Untuk Kelayakan Kredit Menggunakan Association Rule Mining Classifier Di Koperasi Syariah BMT Subulussalam Bantul,” Yogyakarta, 2014.

Z. Yue, W. Lijuan, and W. Ning, “Efficient weighted association rule mining using lattice,” in Control and Decision Conference (2014 CCDC), The 26th Chinese, 2014, pp. 4913–4917.

E. Turban, Decision support and expert systems 4th Ed. Prentice-Hall, Inc., New Jersey, 1995.

B. Le, H. Nguyen, and B. Vo, “Efficient algorithms for mining frequent weighted itemsets from weighted items databases,” in Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2010 IEEE RIVF International Conference on, 2010, pp. 1–6.

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

International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by Informatics Department - Universitas Ahmad Dahlan, and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: ijain@uad.ac.id (paper handling issues)
    info@ijain.org, andri.pranolo.id@ieee.org (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0