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Infectious diseases remain a dynamic and evolving public health threat, requiring data-driven approaches for early detection and targeted policy planning. This study aims to model spatio-temporal trends and clustering patterns of HIV transmission in Bogor Regency during the period 2020–2023 by utilizing a combination of unsupervised and supervised machine learning techniques. The dataset was obtained from the Bogor Regency Health Office and includes annual data on the number of HIV cases across 40 sub-districts. The research methodology consists of data preprocessing stages, clustering using the K-Means algorithm, and classification using a Decision Tree model. The preprocessing steps include data integration, attribute selection, temporal aggregation, handling of missing data, and normalization using Z-score. K-Means clustering is applied to identify hidden patterns in the development of HIV cases, resulting in three distinct clusters based on multi-year trends. The resulting cluster labels are then used as target classes in the supervised classification process. The Decision Tree classification model demonstrates high accuracy in predicting cluster membership, indicating a strong relationship between the temporal patterns of HIV cases and cluster identity. The integration of clustering and classification techniques provides a robust analytical framework for understanding the dynamics of HIV transmission, while also supporting the formulation of more precise, evidence-based, and region-specific public health interventions.

Stunting occurs due to malnutrition which inhibits growth in toddlers. Stunting can also be caused by problems during pregnancy. This study aims to identify the risk of stunting during pregnancy and determine pregnant women who are at risk of this condition. By identifying and prioritizing critical factors that contribute to stunting in children under five, this research is expected to assist policy makers in developing effective solutions to reduce stunting rates. Handling the problem of stunting is important for the Government because it relates to the future generation of Golden Indonesia 2045. This study evaluates appropriate actions or therapies to reduce the risk of having children born with the potential to experience stunting. In the process of selecting pregnant women who are at risk of giving birth to children with the risk of stunting, a selection procedure is carried out that considers several factors such as the mother's age, mother's nutritional intake, arm circumference, hemoglobin level, parity, birth spacing, height, and mother's body mass index (BMI). The analytic network process (ANP) approach is used to determine the outcome of the selection process. The ranking is determined based on the calculation of the weighting of the criteria and sub-criteria in the ANP method. Based on the results of calculations using the ANP approach, PM 1 pregnant women get the highest score and are ranked first. These pregnant women are considered to have the highest risk of giving birth to babies with stunting risk.