2. Literature Review Credit assessment Credit assessment is a study to identify a feasibility of a loan application. It is performed to assess if a potential lender has business activities that are feasible, marketable, profitable and id the laon can be paid on time (Rivai 2006). Usually credit assessment is done by banking account officer which can be a part of an assessment committee. This assessment is done to analyse all factors involved in credit application, such as business financial performance and credit rating of the lenders. Rivai (2006) define 6C's analysis for credit assessment, which are: 1. Character. Character is the lender credit rating, which assess the willingness to pay and the ability to pay based on defined agreement. Character is the most important factor in credit assessment. 2. Capital. Capital is the amount of available initial fund of the lender. The higher the fund, the more the lender is considered to be able to repay the loan. 3. Capacity. Capacity is the ability of the lender to pay the annuity. It is evaluated by the income and the expected income during the period of the loan payment. The capacity is needed to assess the ability to pay from the lender. 4. Collateral. Collateral is lender's properties which should be given to the bank as a warranty for the loan. The collateral should have value similar to the loan applied by the lenders. 5. Condition of Economy. Condition of economy, which is political, social, cultural and economical situation that may cause the business sustainability of the lender. 6. Constraint. Constraint is all limitation and barriers that may make the business cannot continue the activities, such as regulation and resource scarcity. Data mining According to Gartner Group in Larose, data mining (2005) is the process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data sotred in repositories, using pattern recognition technologies as well as statistical and mathematical techniques. There are six phases in standard procedure in performing data mining. Based on Cross - Industry Standard Process for Data Mining (CRISP - DM). Those are (Larose 2005), 1. Business understanding phase, which consist of the determination of business objectives, data mining problem definition, and data mining strategy preparation. 2. Data understanding phase, which consist of the collection of initial data, data exploration, and data evaluation. 3. Data preparation phase, which consists of data selection, data transformation, and data cleaning. 4. Modeling phase which consist of data modeling which main task is to model the data to represent the situation. 5. Evaluation phase, which evaluate the result of the model and to evaluate if the result is in line with the business objectives. 6. Deployment phase is an implementation phase. Figure 1 shows the phases of the data mining standard process. Decision tree is a method in data mining which has association rules to process massive data. Decision tree identifies a collection of variables and associated rules that influence the decision making and analyze how the impact of these variables (Olshon & She 2007). Decision tree is a classification method that creates decision nodes connected with branches which consist of root node and leaf node where the root node is the top node of the decision tree. All attributes in decision node are tested and evaluared to show if they can generate another note, otherwise terminating node is generated (Larose 2005). C5.0 algorithm is the newest version of decision tree methodwhich is the development version of ID3 (Iterative Dichotomiser 3) (Berry & Linoff 1997). This algorithm prunes decision tree model by identifying the error rate in each node and assumes the error rate at the predecessor node as the worst error. The errorr rates are then compared and the decision tree model chosen is the model that has the least error rate (Berry & Linoff 1997). C5.0 algorithm has a decision tree with unlimited nodes. Compared to other akgorithm in decision tree model (such as CART algorithm and heuristic models), this algorithm can provide more branches which resulted in the quicker process in generating nodes. Figure 2 shows the differences process of generating nodes between C.50 and CART algorithms.