The aim of this study is to examine how geographical location affects the credit risk faced by savings and loan cooperatives. Using a quantitative approach, this research will develop a credit risk model that considers geographical variables,measured by the Human Development Index (HDI). The initial stage of the research involves classifying the credit dataset according to the categoriesdetermined by Bank Indonesia. The data cleansing process resulted in attributes such as credit ceiling, HDI, and credit category. Analysis was conducted using Chi-Square, and Logistic Regression methods. The Chi-Square analysis results showed statistically significant relationship between credit ceiling, HDI, and credit category (p-value < 0.05). The Logistic Regression models demonstrated high accuracy in classifying the data, with Logistic Regression achieving 89.71%. In conclusion, credit ceiling and HDI have a significant influence on credit category, with the Logistic Regression model data classification. This study provides valuableinsights into how credit ceiling and HDI influence credit categories, which can be used to make better decisions related to public policy, developmentplanning, and social interventions
