<![CDATA[Credit is the biggest asset carried out by a bank and become the most dominant contributor to the bank income. However, the activity to distribute the credit takes a risk which can influence health and continuance of bank business. The credit risk which potentially occurs can be measured and controlled by analyzing directly the credit client which belongs to current credit or bad credit based on the character in credit assessment, such as age, and amount of loan, how long the relationship between company and bank, the period of company, total income, and debt risk of company to the income. Discriminant analysis is a multivariate statistical technique which can be used to classify the new observation into a specific group. Kernel discriminant analysis is a non-parametric method which is flexible because it does not have to concern about assumption from certain distribution and equal variance matrices as in parametric discriminant analysis. The classification using the kernel discriminant analysis with the normal kernel function with optimum bandwidth 0,1 in data of credit client from bank “X” in Lampung Province gives accurate classififcation 92% whereas kernel discriminant analysis with the epanechnikov function with the optimum bandwidth 4,6 gives the accurate classification 79%. Keywords: credit, classification, kernel discriminant analysis]]>
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