CD Skripsi

ANALISIS CLUSTER PENGANGGURAN DI INDONESIA MENGGUNAKANMETODE SELF ORGANIZING MAP

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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.
Keywords: Cluster analysis, unemployment, standard deviation, SOM.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

1903110776

Penerbit

Pekanbaru : Universitas Riau FMIPA Statistika., 2023

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

1903110776

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

STATISTIKA

Info Detail Spesifik

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Pernyataan Tanggungjawab

ERA

Versi lain/terkait

Lampiran Berkas

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