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Anindya Apriliyanti Pravitasari
Departemen Statistika, Universitas Padjadjaran, Indonesia

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Analisis Biplot Pada Pengelompokan Kecamatan Di Kabupaten Tasikmalaya Berdasarkan Indikator Kemiskinan Annisa Siti Utami; Anindya Apriliyanti Pravitasari; Irlandia Ginanjar
Inferensi Special Issue: Seminar Nasional Statistika XI 2022
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v1i1.19128

Abstract

Poverty is a social problem that continues to exist in people's lives according to Nurwati, 2008. Therefore, the problem of poverty is the center of attention of the Tasikmalaya Regency government. In the National Long-Term Development Plan (RPJPN) 2005-2025 the problem of poverty is seen in a multidimensional framework, therefore poverty is not only related to income measurement, but related to several things. This is because poverty is not only related to the size of income but involves several things. In the Tasikmalaya Regency Regional Medium-Term Development Plan (RPJMD), the target for achieving the poverty rate in 2021 is 10.23%. Based on BPS publications, there are 10.75% of the population of Tasikmalaya Regency who are categorized as poor, meaning that the Tasikmalaya Regency government's target has not been achieved. So it is necessary to make efforts to overcome the problem of poverty. This study aims to group sub-districts in Tasikmalaya Regency based on the similarity of poverty indicators owned by each sub-district by using biplot analysis. The data used is poverty indicator data for 39 sub-districts in Tasikmalaya Regency in 2021. From the research results it is known that the amount of variation that can be described is 97%, meaning that the plots formed can best describe actual conditions. data information. In addition, three clusters have the same poverty indicators. Cluster 1 contains sub districts that have an indicator in the form of a high student to school ratio in SMA/SMK/MA. Cluster 2 contains sub districts that have moderate to low indicators on all variables except the ratio of SMP/MTs students and the ratio of SMA/SMK/MA students. Meanwhile, Cluster 3 consists of sub-districts that have an indicator in the form of a high ratio of SMP/MTs students.