IMPACT OF URBAN DEVELOPMENT ON UV EXPOSURE: A CLUSTERING AND MACHINE LEARNING ASSESSMENT

The relocation of Indonesia's capital city is anticipated to promote inclusive economic growth while embracing cultural diversity. However, this transition may affect ultraviolet (UV) radiation exposure patterns. The study investigated variations in UV exposure in the IKN region, focusing on urban development factors such as land use and population density that affect public health, sun protection, and skin cancer prevention. The research hypothesized that UV radiation is significantly correlated with these factors. UV Index data from 2010-2023, a hierarchical clustering method, identifies complex data patterns without determining the number of clusters. XGBoost, a machine learning model, was used for handling high-dimensional data and strong non-linear interactions, outperforming Random Forest in predicting Ultraviolet A variables. Analysis of variance (ANOVA) showed significant inter-group differences, which were validated by Tukey HSD post-hoc tests. Results showed that Cluster 4 was the region with the highest UV exposure. In contrast, Cluster 5 recorded the lowest, with exposure levels ranging from 6.61 to 15.82, a considerable difference of 9.21. The findings underscore the role of geographic and environmental factors in shaping UV exposure patterns, with implications for public health. Areas with high UV exposure face higher risks, including skin cancer and premature ageing. The predictive accuracy of the XGBoost model highlights its usefulness in addressing UV-related health risks. The study advocates for improved UV protection strategies and informed health policies to mitigate climate change impacts and promote sustainable urban development. The findings suggest that the development of data-driven early warning systems for UV radiation exposure could be implemented to improve public health policy and safety.