Feasibility study for banking loan using association rule mining classifier

Koperasi syariah growing rapidly in Indonesia. The activity of this koperasi is the provision of credit to small businesses and the poor. The Koperasi get greatest revenue from loans, buying and selling. Procedures to apply for a loan is to fill the application form and give some documents, such as personal data last, the data that is being run business and financial reports, family conditions / environment prospective partner. The data is used to predict whether the partners can complete pay the credit (repay financing) to the end or not.


I. Introduction
Koperasi syariah growing rapidly in Indonesia.The activity of this koperasi is the provision of credit to small businesses and the poor.The Koperasi get greatest revenue from loans, buying and selling.Procedures to apply for a loan is to fill the application form and give some documents, such as personal data last, the data that is being run business and financial reports, family conditions / environment prospective partner.The data is used to predict whether the partners can complete pay the credit (repay financing) to the end or not.
Problems happened when the number of members and the number of credit applicants increasing.Based on interviews with the Koperasi BMT Subulussalam Bantul, Yogyakarta Indonesia, the most koperasi in Bantul area, there are about 60 similar koperasi and a quarter of them have bad credit problems.
Koperasi must identify the characteristics of prospective creditors troubled by studying the pattern of the troubled lender characteristics of existing customer data and the information from previous lending experience.Then, these patterns can be used as learning, if there is a borrower or lender has characteristics in common with the previous customer problems is the management of Koperasi will gain a valuable consideration for a more detailed and more careful in deciding the appropriateness customers receive credit funds.
One of informatics method to recognize patterns in the Koperasi data members, called data mining methods.Appropriate data mining methods used to identify characteristic patterns of prospective lenders are the methods of classification.This research uses a classification method, called Association Rule Mining Classifier.The results of this method is a characteristic pattern of troubled lenders.Classification must recognize new members who apply for credit by classifying new members into two groups, include 1) good credit, and 2) bad credit groups.If a potential creditor can be classified based on their profile earlier then it will be considered to pass a creditor receives money loan and this will certainly reduce the risk of bad debts.
Agus Sasmito Aribowo and Nur Heri Cahyana (Feasibility study for banking loan using association …) This research aims to develop a decision support system model for classification the credit application to reduce the risk of bad debts using Association Rule Mining classifier method.

II. Related Work
Applied research related to data mining methods for bank credit has been investigated by several researchers.Data mining classification and bi-level programming were used for optimal credit allocation, and findings show that although the objective functions of the leader are worse in the bilateral scenario but agent banks collaboration is attracted and guaranteed [1].Data mining for credit worthiness using decision tree.C4.5 algorithm used to classify the customer profile of bank credit.The classification is determined by the rate of return risk of credit with 6 criteria, namely: gender, occupation, income, savings, mortgage credit, and education.Data input is derived from credit transactions database.The application is also able to perform analysis or prediction to determine the success of the credit [2].Application of the method of logistic regression modeling to predict credit risk on an individual basis [3] .Another study is the comparing three credit application scoring model is a model logistic regression (LR), models of classification and regression tree (CART) and a model of neural network (NN) to classify the credit application will be accepted and rejected.The results showed that the Neural Network models more valid and accurate [4], association rule mining with clustering used for loss profit estimation [5].Research the use of WIT Tree for classification was also performed by Cahyana and Aribowo [6].However, these studies used three dummy data as data set.An effective approach for mining weighted association rules from the weighted transaction database have been proposed, the results is finding the shorter average execution times, and also could remove some redundant rules [7].

A. Decision support system
Decision Support System (DSS) proposed in the early 1970s by Scott Morton, who articulate the important concept of decision support systems.The concept of a decision support system is characterized by a computer-based interactive system that helps decision-making by utilizing the data module to select issues that are not structured [8].Keen and Scott Morton argues that the decision support system is a computer-based support system for management decision makers who deal with the problems of unstructured, decision support systems combine the intellectual resources of individuals with computer capabilities to improve the quality of decisions [8].Decision Support System is built to support a solution to a problem or to evaluate an opportunity.DSS as it is called DSS applications.
B. Association rule mining using WIT tree 1) Algoritma WIT-FWIs ( Weight Itemsets Tidset-Frequent Weight Itemsets) Apriori algorithm does not consider the divergence of interests every items in the transaction [9].All item in the itemset have same interest's value.Example bread that give profit $3 considered equal with milk that give profit only $1.5.Le et al. applied new strategy in mining frequent item set.The strategy is called frequent weight item sets, which every item have different interest's value.
Algoritma WIT-FWIs consist of two basic concepts, weight itemset tid-set tree (WIT-Tree) data structure and frequent weight item sets (FWIs).WIT-Tree is tree data structure for exploring frequent weight item sets and FWIs for processing frequent weight item sets [9].

2) WIT-Tree ( Weight Itemsets Tidset Tree)
WIT-Tree algorithm only scan the database once, because the next itemset can built from the intersection Tidsets and it can used to compute weight support for the next step [9].It can reduce time reading large database.Before built WIT-Tree, the process must find weight item value in every transaction.If D is database and in its database containt transaction ܶ = ሼ‫ݐ‬ ଵ , ‫ݐ‬ ଶ , ‫ݐ‬ ଷ , … , ‫ݐ‬ሽ , every transaction t consist itemset ‫ܫ‬ = ሼ݅ ଵ , ݅ ଶ , ݅ ଷ , … , ݅ ሽ.Every item have weight ܹ = ሼ‫ݓ‬ ଵ , ‫ݓ‬ ଶ , ‫ݓ‬ ଷ , … , ‫ݓ‬ ሽ corresponds with every item in ‫,ܫ‬ then the transaction weight can be calculated.Transaction weight ‫)ݓݐ(‬ every transaction ‫ݐ‬ , defined as [9]: Transaction weight ‫)ݓݐ(‬ in every transaction ‫ݐ‬ in T, where T is transaction list in database defined as [9]: Example processing ws(x) using transaction itemset shown on Table 1 and weight on

IV. Results and Discussion
Data mining models to identify the characteristics of members who successfully complete the debt repayments and then generate the rule for predicted other members described in Fig. 2. The model requires the input of training data that example as table 4. Training data will be formed into WIT-tree.Then it will be processed using Frequent Weight Itemset to select the itemset that exceeded the minimum support.Itemset thet ws(x) larger than minimum support called frequent itemset, and it is become the knowledge that will be used to classify the data testing.Knowledge rules as in table 5 and the data testing in Table 6.Attribute class indicates success on the relevant credit.The second step a data mining process from Table 5 with the association rule mining algorithm, WIT Tree.The result of WIT Tree process is the rules (Table 6).

Table 2 .
Weighted Items

Table 3 .
Transaction weight of all transaction in

Table 1 Transaction ID Transaction Weight (tw)
Vertex is node-node in WIT Tree.Vertex x is build by user, and ‫)ݔ(ݐ‬ is transaction contain ‫ݔ‬ and ‫ݏݓ‬ is weight support ‫,ݔ‬ vertex furmulated by : Illustration the WIT Tree with minimum weight support=0,4 shown on Figure1. b. Determining the line from one vertex to another vertex at lower level.Example node A will joined with node {B}, {C},{E} and then ሾ‫ܣ‬ሿ = ሼሼ‫ܤܣ‬ሽ, ሼ‫ܥܣ‬ሽ, ሼ‫ܣ‬D},{AE}}.ሾ‫ܤܣ‬ሿwill be class equivalence class if join with node ሼ‫ܥܣ‬ሽ, ሼ‫ܦܣ‬ሽ, ሼ‫ܧܣ‬ሽ.Agus Sasmito Aribowo and Nur Heri Cahyana (Feasibility study for banking loan using association …)

Table 4 .
Dataset customer profile (as data training)

Table 5 .
The rule result from WIT-Tree process