<![CDATA[The success of national development is determined not only by economic growth but also by the quality of human development, which is measured through the Human Development Index (HDI). Although Indonesia's HDI has continued to improve, regional disparities in Western Indonesia remain a significant development challenge. This study aims to classify and analyze the factors influencing HDI by integrating the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Geographically Weighted Regression (GWR) methods with adaptive Gaussian weighting. Class imbalance in the training data was addressed using the Synthetic Minority Oversampling Technique (SMOTE), while model performance was evaluated using a confusion matrix, accuracy, precision, recall, and F1-score. The results show that SVM with a Radial Basis Function (RBF) kernel outperformed KNN, achieving an accuracy of 78.8%, a precision of 0.89, a recall of 0.78, and an F1-score of 0.82. In comparison, KNN achieved an accuracy of 76.9%, a precision of 0.89, a recall of 0.76, and an F1-score of 0.80. Furthermore, the GWR analysis identified 16 spatial clusters characterized by different dominant factors, including population size, the Community Literacy Development Index, per capita expenditure, the open unemployment rate, and the number of sub-districts. The GWR model produced a coefficient of determination (R²) of 84.17%, indicating strong explanatory power. These findings demonstrate that the integration of machine learning techniques and GWR is effective in classifying HDI and revealing spatial variations in human development factors, providing valuable insights for the formulation of more targeted development policies aimed at reducing regional HDI disparities.]]>
