ANALISIS CLUSTER PENGANGGURAN DI INDONESIA MENGGUNAKAN METODE SELF ORGANIZING MAP
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Date
2023-07
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Elfitra
Abstract
Unemployment is a situation where someone is not working or is trying to find a job but
unable to find work. The spread of unemployment in Indonesia has different
characteristics in each region, so it is necessary to classify the unemployed so that each
government policy program can be carried out in a more focused and directed manner.
This study discusses cluster analysis of unemployment using the Self Organizing Map
(SOM) method in classifying the unemployed in Indonesia in 2020. The SOM method
is able to show dominant patterns and variables in clusters. The variables used in this
study consisted of school enrollment rates, average length of schooling, labor force
participation rates, and the percentage of the population using computers. The results of
this study formed 3 unemployment rate clusters with cluster 1 being a low
unemployment group consisting of 144 districts/cities, cluster 2 with a medium level
consisting of 287 districts/cities, and cluster 3 with a high level consisting of 83
districts/cities. The grouping using the SOM method on district/city unemployment data
in Indonesia is good because it has a minimum standard deviation ratio of 0.529.
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Keywords
Cluster analysis, unemployment, standard deviation, SOM
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