Rohmatulillah, Oktaviana Nur
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Analisis Klaster pada Faktor-Faktor yang Mempengaruhi Indikator Kesejahteraan Sosial dan Ekonomi di Provinsi Jawa Timur Tahun 2023 Rohmatulillah, Oktaviana Nur; Nirmala, Karisma Bunga; Wulandari, Sri Pingit
Jurnal Ekonomi, Bisnis dan Manajemen Vol. 3 No. 4 (2024): Desember : Jurnal Ekonomi, Bisnis dan Manajemen (EBISMEN)
Publisher : FEB Universitas Maritim Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/ebismen.v3i4.2784

Abstract

Social and economic welfare reflects the quality of life in a region and is influenced by local social, economic, and environmental factors. East Java, as the second most populous province in Indonesia, faces challenges in improving the welfare of its residents, particularly due to varying regional characteristics such as employment, education, and population demographics. To understand the patterns of interrelationships among factors affecting welfare, this study conducted a klaster analysis to group regions based on similar characteristics. The klaster analysis employed both hierarchical (complete linkage) and non-hierarchical (K-means) approaches to determine the optimal number of klasters. The results revealed that the level of diversity across regions in East Java tends to be homogeneous in social and economic aspects, with average values exceeding standard deviations. Assumption tests for the klaster analysis confirmed that the data met the assumptions of multivariate normal distribution and dependency.Through hierarchical (complete linkage) and non-hierarchical (K-means) klaster analysis, two main klasters were formed, dividing districts/cities in East Java based on welfare characteristics. Using the complete linkage method, 27 regions were grouped into klaster 1, and 11 regions into klaster 2, while K-means grouped 26 regions into klaster 1 and 12 regions into klaster 2. Out of the six variables used, one variable was found to be insignificant in influencing the klastering results. Based on the mapping results, the grouping aligns with similar criteria, where urban areas predominantly fall into one klaster, and the other klaster is dominated by rural areas.