<![CDATA[Drug abuse’s a serious problem leading to addiction and harmful effects. Rehabilitation aims to save drug addicts and help them lead a normal, physically, and mentally healthy life while improving skills and social relationships. The North Sumatra Province National Narcotics Agency’s responsible for drug prevention, eradication, and rehabilitation. There’re different rehabilitation programs for adolescents and adults, with a "parenting" program applied for adolescents. However, the manual and inefficient process of determining programs poses challenges. This research utilizes data mining with the K-Means clustering algorithm to efficiently categorize drug rehabilitation patient data. The clustering results in three patient clusters based on their characteristics, providing essential information for North Sumatra Province National Narcotics Agency to tailor rehabilitation programs to each group's needs. Through the data clustering process, drug user patterns can be identified based on their shared attributes. Consequently, The North Sumatra Province National Narcotics Agency can determine more effective and suitable programs for each cluster. The findings show that the parenting program is appropriate for Cluster two. The study concludes that using the K-Means clustering algorithm with Python and Jupyter Notebook tools yields optimal clustering results. This research serves as a foundation for application development, further investigations, and comparisons with other clustering algorithms in drug rehabilitation patient data grouping.]]>
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