<![CDATA[Credit risk assessment is a crucial component of the banking system because it directly relates to a financial institution's ability to manage potential losses due to non-performing loans. Banks often face difficulties in accurately classifying customer credit risk levels, especially when the data being analyzed is complex, nonlinear, and contains interacting variables. Conventional methods such as regression analysis often fail to capture hidden patterns in such data. Therefore, this study aims to apply the Support Vector Machine (SVM) algorithm as a solution to classify bank customers' credit risk levels based on attributes such as income, loan amount, length of employment, payment history, debt-to-income ratio, and asset ownership status. The research process begins with data collection and pre-processing, including data cleaning and normalization to ensure a uniform distribution of values. The data is then divided into training and test data with specific proportions. An SVM model is then applied using several kernel types, such as linear, polynomial, and radial basis function (RBF), to determine the best-performing kernel. Model evaluation is performed using accuracy, precision, recall, and F1-score metrics to measure classification performance. Test results show that the SVM model with the RBF kernel provided the best results, achieving an accuracy rate of over 90% and minimizing classification errors in the high-risk category. In conclusion, the application of the SVM algorithm has proven effective in classifying customer credit risk levels with high accuracy and stability, making it a reliable tool for banks in the creditworthiness analysis process and more accurate, data-driven strategic decision-making]]>
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