Rw Segmentation Analysis for the Climate Village Program as a Basis for Planning in South Jakarta Using K-Means Clustering - Marni Berek, Maria Susey; Taufiq, Ghofar; Chrisnawati, Giantika (Blueprint Journal, 2025)
Climate change is a global issue that has multidimensional impacts on human life, including in Indonesia. In response to this challenge, the government developed the Climate Village Program (PROKLIM) which prioritizes community empowerment through a community-based approach. This program aims to strengthen climate change adaptation and mitigation efforts through participatory local resource management. This study uses the K-Means clustering method to group areas based on environmental characteristics at the Neighborhood Association (RW) level, in order to identify patterns and support decision making in effective environmental management. This study proves that the K-Means Clustering method is effective in grouping RWs in South Jakarta based on indicators relevant to the Climate Village Program (ProKlim). The latest report from the World Meteorological Organization (2024) states that 2023 was the hottest year in history, with an anomaly (Hasbullah & Assyahri, 2025) of global temperatures reaching 1.45°C above the average temperature in the pre-industrial era. Furthermore, the last nine years (2015–2023) were recorded as the period with the hottest consecutive temperatures in the history of climate records. The segmentation results show clear differences between groups in terms of levels of vulnerability to climate change, community engagement, and environmental preparedness. This grouping provides a strong, data-driven analytical basis, allowing the South Jakarta Environmental Agency (DLH) to use it as a strategic reference for more targeted and targeted planning and implementation of ProKlim.