Comparison of K-Means and K-Medoids Methods in Clustering High Population Density Areas in Bireuen Regency - Andri Alfitra; Nurdin, Nurdin; Meiyanti, Rini (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 2025)
This study examines the population density distribution in Bireuen Regency by applying two clustering algorithms, namely K-Means and K-Medoids, to demographic data from 2019 to 2023. Three main variables were used: total population, number of ID card holders (KTP), and number of households (KK). The clustering results identified three primary groups: very dense, dense, and not dense. Districts such as Kota Juang, Jeumpa, and Peusangan consistently fell into the very dense category, while districts like Pandrah, Gandapura, and Makmur tended to be classified as not dense. Cluster quality was evaluated using the Davies-Bouldin Index (DBI). The evaluation results showed that the K-Means algorithm performed better in most years analyzed, particularly in 2020 with the lowest DBI value of 0.3906. Meanwhile, in 2023, K-Medoids outperformed K-Means, with a DBI value of 0.7724. These findings indicate that K-Means is more effective in handling homogeneous data, whereas K-Medoids is more adaptive to data containing outliers or irregular patterns. Overall, the choice of clustering method depends on the characteristics of the data used. The results provide a spatial overview of population distribution that can support regional planning and data-driven public policy. These findings are expected to serve as a basis for more targeted and equitable regional development planning. For future research, it is recommended to expand the analysis by including additional variables such as area size and socioeconomic indicators, as well as optimizing the number of clusters using methods like the Elbow method or Silhouette Score.