Mapping Indonesia’s Regions Based on Carbon Emissions Using the K-Means Algorithm
William William, Luhur Bayuaji, Novario Jaya Perdana, Teny Handhayani · 2025
Carbon emissions, also known as greenhouse gas (GHG) emissions, refer to the release of gases that trap heat in the Earth’s atmosphere, contributing to the greenhouse effect and climate change. These gases, primarily carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) are produced through various human activities. GHGs are generated from sectors such as energy, industry, agriculture, forestry, and waste, each contributing emissions with distinct characteristics in every region. GHG data from 34 provinces in Indonesia from 2000 to 2023 were processed using the K-Means algorithm for clustering to facilitate the analysis of emission patterns, Clustering was based on the similarity of emission characteristics across these five sectors. Clustering results were evaluated using the Silhouette Coefficient to assess the quality of the grouping. Visualization in an inter-active map allows users to understand the distribution patterns of emissions between provinces. The analysis process includes several stages from steps of data collection, data preprocessing, clustering, evaluation, and visualization. The K-Means algorithm has proven effective in grouping provinces based on the similarity of GHG emission profiles in each sector as well as combined sectors. Evaluation using the Silhouette Coefficient showed that clustering data into three clusters obtained an average score of 0.62. This result indicates a medium level of similarity among provinces within a cluster. Riau Province was identified as the highest emitter, while Papua and West Papua were recognized as the provinces acting as the highest absorbers. The interactive map successfully demonstrated the spatial distribution of emissions.