ANALISIS CLUSTER PENGANGGURAN DI INDONESIA MENGGUNAKAN METODE SELF ORGANIZING MAP

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dc.contributor.authorGunawan, Chairani
dc.contributor.supervisorSirait, Haposan
dc.date.accessioned2023-10-20T07:41:00Z
dc.date.available2023-10-20T07:41:00Z
dc.date.issued2023-07
dc.description.abstractUnemployment 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.en_US
dc.description.sponsorshipFakultas Matematika dan Ilmu Pengetahuan Alam Universitas Riauen_US
dc.identifier.citationPerpustakaanen_US
dc.identifier.otherElfitra
dc.identifier.urihttps://repository.unri.ac.id/handle/123456789/11177
dc.language.isoenen_US
dc.publisherElfitraen_US
dc.subjectCluster analysisen_US
dc.subjectunemploymenten_US
dc.subjectstandard deviationen_US
dc.subjectSOMen_US
dc.titleANALISIS CLUSTER PENGANGGURAN DI INDONESIA MENGGUNAKAN METODE SELF ORGANIZING MAPen_US
dc.typeArticleen_US

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