Comparison of K-Means, Fuzzy C-Means, Fuzzy Gustafson Kessel, and DBSCAN for Village Grouping in Surabaya Based on Poverty Indicators
Abstract
The population growth rate in various countries in the world is increasing, including Indonesia. The population explosion as a result of rapid population growth has a negative impact on the socio-economic life of the community, such as increasing unemployment rates, food shortages, and high poverty rates. Therefore, local governments in each country try to overcome the poverty problem using various policies, including in Surabaya, East Java, Indonesia. This study aims to classify villages in Surabaya using non-hierarchical clustering, such as K-Means, Fuzzy C-Means, Fuzzy Gustafson Kessel, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise), based on poverty indicators. Before analysis, the villages in Surabaya, East Java, Indonesia were classified using non-hierarchical clustering, and the results of cluster analysis were compared from various methods using the value of within clusters sum of squares and average silhouette width. Comparison between village grouping methods results in K-Means being the best method for village grouping in Surabaya, East Java, Indonesia based on the values of the within clusters sum of squares. While based on the average silhouette width value, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method is found to be the best method because its value was close to 1 compared to the other methods. Thus, it can be concluded that K-Means and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is the best method for village grouping in Surabaya, East Java, Indonesia in relation to poverty problems.
Laju pertumbuhan penduduk di berbagai negara di dunia semakin meningkat, termasuk Indonesia. Ledakan penduduk akibat pertumbuhan penduduk yang pesat berdampak negatif terhadap kehidupan sosial ekonomi masyarakat, seperti meningkatnya angka pengangguran, kekurangan pangan, dan tingginya angka kemiskinan. Oleh karena itu, pemerintah daerah di setiap negara berusaha mengatasi masalah kemiskinan dengan berbagai kebijakan, termasuk di Surabaya, Jawa Timur, Indonesia. Penelitian ini bertujuan untuk mengklasifikasikan desa-desa di Surabaya, Jawa Timur, Indonesia menggunakan non-hierarchial clusterings, seperti K-Means, Fuzzy C-Means, Fuzzy Gustafson Kessel, dan DBSCAN (Density-Based Spatial Clustering of Applications with Noise), berdasarkan indikator kemiskinan. Sebelum dilakukan analisis, desa-desa di Surabaya, Jawa Timur, Indonesia diklasifikasikan menggunakan non-hierarchical clustering, dan hasil analisis cluster dibandingkan dari berbagai metode dengan menggunakan nilai cluster sum of squares dan rata-rata lebar siluet. Perbandingan antar metode pengelompokan desa menghasilkan K-Means menjadi metode terbaik untuk pengelompokan desa di Surabaya berdasarkan nilai cluster sum of squares. Sedangkan berdasarkan nilai rata-rata lebar siluet, metode DBSCAN (Density-Based Spatial Clustering of Applications with Noise) merupakan metode yang paling baik karena nilainya mendekati 1 dibandingkan dengan metode lainnya. Dengan demikian, dapat disimpulkan bahwa K-Means dan DBSCAN (Density-Based Spatial Clustering of Applications with Noise) merupakan metode terbaik untuk pengelompokan desa di Surabaya, Jawa Timur, Indonesia dalam kaitannya dengan masalah kemiskinan.
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DOI: http://dx.doi.org/10.21043/jpmk.v5i2.16552
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