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All Journal UNP Journal of Statistics and Data Science
Hefiani Mustika Hasanah
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Fuzzy Geographically Weighted Clustering Method for Grouping Provinces in Indonesia Based on Welfare Indicators Aspects of Information and Communication Technology (ICT) Hefiani Mustika Hasanah; Dina Fitria; Dony Permana; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol1-iss5/108

Abstract

The welfare of the people is a task and goal that must be realized by the Republic of Indonesia. To find out the condition of the welfare of the Indonesian people, it can be seen in eight areas of Indonesia's welfare indicators. Indicators The welfare of the Indonesian people is undergoing a digital transformation of information and communication technology (ICT) in 2021. However, there was a gap in ICT development due to geographical conditions and the distribution and dynamics of each region's society. Cluster analysis is a solution for target setting for better future decisions. Fuzzy Geographically Weighted Clustering (FGWC) is one of the cluster methods with fuzzy logic that considers geographical and population elements in grouping targets. The results of the research resulted in three optimum clusters with different characteristics for  each cluster based on indicators of ICT aspects of people's welfare. Cluster 1 has a medium status of ICT indicators of people's welfare and is located in the middle or at the end of the island, provinces from cluster 2 have a low status of ICT indicators of people's welfare with a medium area, while cluster 3 has a high status of ICT indicators of people's welfare with a large area or dense populations.
Co-Authors Dina Fitria Dony Permana Zamahsary Martha