<![CDATA[Water is essential for meeting the basic needs of living organisms. In Indonesia, ensuring safe and quality drinking water is crucial for public health. However, in some regions, particularly in West Java Province, people still rely on unsuitable water sources, which can negatively impact health. The classification of water source suitability can be achieved using machine learning, such as the Extreme Gradient Boosting (XGBoost) model. XGBoost with feature selection is effective in improving prediction accuracy and minimizing overfitting. This study evaluates the performance of the XGBoost model in classifying household drinking water sources in West Java and uses the K-Means algorithm for cluster SHAP values to identify key characteristics of households with safe drinking water. The results show that the XGBoost model, with an accuracy of 77.43% and an F1-Score of 80.17%, successfully classified 4187 households, with 2349 having safe drinking water and 1838 having unsuitable sources. SHAP value analysis identified location, water collection time, and monthly per capita expenditure as significant factors influencing water source suitability. Households with water sources inside the house's fence, a short water collection time, and high monthly per capita expenditure tend to have safe drinking water sources. There are 4 clusters formed, with cluster 1 and cluster 3 needing immediate quality of drinking water sources improvement with cluster 2 as an indicator of success. Cluster 4 consists of households with high expenditure, marking it as a potential household for the government to make water quality improvements.]]>
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