<![CDATA[Waste is the solid residue of human activities or natural processes that is considered useless. If not managed properly, it can have a negative impact on the environment and health. When waste management facilities such as transport fleets, waste banks, and TPS3R (3R waste management sites) are insufficient to handle the volume of waste effectively, waste accumulation will occur, which can pollute the air, soil, and water, and increase the risk of disease spread. Therefore, new data-driven thinking is needed to improve more targeted and efficient waste management. The application of the K-Means clustering technique in waste management can be done, as demonstrated by the results of regional clustering in Bogor Regency with recommendations for appropriate facilities. The use of variables such as uncollected waste volume, distance to landfill sites, number of villages and population, as well as the stages of determining the number of clusters, initial centroid point determination, calculation of data distance to centroid points, and grouping of data according to minimum distance to centroid points were carried out to produce the clustering. The variables and stages were then applied to a prototype decision support system to assist the Bogor District Environmental Agency in placing waste management facilities more effectively. The prototype system developed has undergone]]>
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