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

Abstract


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

Keywords


Association Rule Mining Classifier; WIT-Tree; Banking Loan

   

DOI

https://doi.org/10.26555/ijain.v1i1.8
      

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