ANALISIS MULTIDIMENSIONAL SCALING DAN CLUSTER FUZZY C-MEANS PADA DATA INDIKATOR KESEJAHTERAAN RAKYAT DI INDONESIA
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Date
2022-04
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Elfitra
Abstract
The Covid-19 pandemic has an impact on the level of people's welfare in regional
segmentation. Graphical and clustering can be done to determine the level of welfare of
the people in Indonesia during the pandemic so that segmental policies can be taken. This
study uses the Central Bureau of Statistics data, namely indicators of the welfare of the
people in Indonesia which consists of GRDP value, per capita expenditure, percentage of
poor people, level of open space, and average length of schooling. Graphical analysis
using multidimensional scaling produced 2 groups of provinces consisting of 30 and 4
provinces and of regencies/cities consisting of 494 and 20 regencies/cities. Validation of
multidimensional scaling of STRESS values close to 0 and R square equal to 1. Clustering
using fuzzy C-Means for welfare status by province has 2 clusters consisting of 30 and 4
provinces and by regencies/cities consisting of 495 and 19 regencies/cities. Comparison
of each variable obtained cluster 2 is more prosperous than cluster 1. Cluster validation
using MPC and FSI is close to 1.
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Keywords
Welfare indicators, multidimensional scaling, validation of multidimensional scaling, fuzzy C-Means, validation of clusters.
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