ANALISIS KLASIFIKASI NASABAH KREDIT MENGGUNAKAN BOOTSTRAP AGGREGATING CLASSIFICATION AND REGRESSION TREES (BAGGING CART)

*Desy Ratnaningrum  -  Universitas Diponegoro
Moch. Abdul Mukid  -  Universitas Diponegoro
Triastuti Wuryandari  -  Universitas Diponegoro
Published: 31 Jan 2016.
Open Access

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Abstract

Credit is one of the facilities provided by banks to lend money to someone or a business entity within the prescribed period. The smooth repayment of credit is essential for the bank because it influences the performance as well as its presence in daily life. Acceptance of prospective credit customers should be considered to minimize the occurrence of bad credit. Classification and Regression Trees (CART) is a statistical method that can be used to identify potency of credit customer status such as current credit and bad credit. The predictor variables used in this study are gender, age, marital status, number of children, occupation, income, tenor / period, and home ownership. To improve the stability and accuracy of the prediction were used the Bootstrap Aggregating Classification and Regression Trees (Bagging CART) method. The classification of credit customers using Bagging CART gives the classification accuracy 81,44%.

 

Key words : Credit, Bootstrap Aggregating Classification and Regression Trees (Bagging CART), Classification Accuracy

Keywords: Credit; Bootstrap Aggregating Classification and Regression Trees (Bagging CART); Classification Accuracy

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