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Journal > International Journal of Advances in Intelligent Informatics > Feasibility study for banking loan using association rule mining classifier

 

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International Journal of Advances in Intelligent Informatics
Vol 1, No 1 (2015): March 2015
Feasibility study for banking loan using association rule mining classifier
Article Info   ABSTRACT
Published date:
31 Mar 2015
 
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
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