Studi Pengelompokan Multimetode Provinsi di Sumatera Utara Menggunakan Pendekatan PCA dan K-Means
Abstract
This study aims to classify regions in North Sumatra based on a set of social and economic indicators by applying a multi-method clustering approach. Principal Component Analysis (PCA) is employed to reduce data dimensionality and identify the most influential variables, while the K-Means algorithm is used to form clusters based on similarity of characteristics. The results indicate that the combination of PCA and K-Means can cluster provinces or regions more efficiently and interpretably. The resulting clusters reflect patterns of similarity among regions in terms of social and economic development, thus providing a basis for formulating more targeted regional development policies. These findings demonstrate that a multi-method approach can yield more comprehensive results in spatial data clustering.
Keywords: Clustering, Principal Component Analysis (PCA), K-Means, multi-method, North Sumatra.
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DOI: http://dx.doi.org/10.30829/algoritma.v9i2.25017
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