Penerapan Algoritma K-means Clustering dan Hierarchical Clustering dalam Mengelompokkan Data Pengangguran di Karawang - Mulyana, Assyifa Alif Rahayu; Juwita, Ayu Ratna; Siregar, Amril Mutoi; Fauzi, Ahmad (Institut Teknologi Garut, 2024)
The existence of various new industrial areas in Karawang can trigger residents from outside the area to migrate so that this will increase the number of residents in Karawang. The increase in population can affect the unemployment rate in an area. To group data, you can use data mining techniques. The K-Means Clustering and Hierarchical Clustering algorithms have not been used to group unemployment data, so this research aims to group unemployment data with these two algorithms. The results are that the K-Means Clustering and Hierarchical Clustering algorithms can group data based on similar characteristics with the same number of clusters but have differences in data distribution within the clusters. The evaluation method with Silhouette Score shows that the two algorithms have the same performance in the analysis in this study.