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GROUPING REGENCIES/CITIES IN WEST JAVA PROVINCE BASED ON PEOPLE’S WELFARE INDICATORS USING BIPLOT AND CLUSTERING

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 18 No. 3 (2024) · hlm. 1839–1852

DOI
10.30598/barekengvol18iss3pp1839-1852
ISSN
2615-3017, 1978-7227
Terbit
2024-07-31
Penerbit
PATTIMURA UNIVERSITY
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The level of people's welfare in West Java Province still requires improvement in each indicator. People's welfare indicators include poverty, employment, education, housing, consumption patterns, health, and population. The level of people's welfare can be known by reviewing all dimensions based on linear relationships between regencies/cities to produce information on indicators that still need improvement. These efforts can assist the West Java Provincial Government determine regional policies and programs for equitable distribution and improve people's welfare in all regencies/cities. The data used in this study are secondary data derived from the Website of the BPS of West Java Province 2023, West Java Open Data Province 2023, and Diskominfo Statistics Division (Jabar Digital Service). The grouping of regencies/cities was done using Principal Component Analysis based on Singular Value Decomposition biplot analysis, and it continued with Ward's Method Clustering based on Euclidean distance calculation. The analysis results formed four groups with different people's welfare indicators characteristics. The group that needs top priority in improvement is group 2 because it has a low level of people's welfare. Cluster 1 contains regencies/cities with high people's welfare characteristics in the housing and employment indicators. Cluster 3 includes regencies/municipalities with high people's welfare characteristics in the consumption pattern level, poverty, employment, and health indicators. Cluster 4 contains cities with high people's welfare characteristics in education and population indicators.

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Rekan penulisOrganisasiArtikel bersama
Ashilla Maula HudzaifaPadjadjaran University1
Valerie Vincent YangPadjadjaran University1
Nabila KarinPadjadjaran University1
Gumgum DarmawanPadjadjaran University1
Anindya Aprilianti PravitasariAfiliasi tidak diketahui1
Kennedy MarthendraPadjadjaran University1
Brian Riski Jayama SimanjuntakPadjadjaran University1

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Sumber metadata: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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	<dc:title xml:lang="en-US">GROUPING REGENCIES/CITIES IN WEST JAVA PROVINCE BASED ON PEOPLE’S WELFARE INDICATORS USING BIPLOT AND CLUSTERING</dc:title>
	<dc:creator>Puspitasari, Priscilla Ardine</dc:creator>
	<dc:creator>Faidah, Defi Yusti</dc:creator>
	<dc:creator>Hendrawati, Triyani</dc:creator>
	<dc:description xml:lang="en-US">The level of people's welfare in West Java Province still requires improvement in each indicator. People's welfare indicators include poverty, employment, education, housing, consumption patterns, health, and population. The level of people's welfare can be known by reviewing all dimensions based on linear relationships between regencies/cities to produce information on indicators that still need improvement. These efforts can assist the West Java Provincial Government determine regional policies and programs for equitable distribution and improve people's welfare in all regencies/cities. The data used in this study are secondary data derived from the Website of the BPS of West Java Province 2023, West Java Open Data Province 2023, and Diskominfo Statistics Division (Jabar Digital Service). The grouping of regencies/cities was done using Principal Component Analysis based on Singular Value Decomposition biplot analysis, and it continued with Ward's Method Clustering based on Euclidean distance calculation. The analysis results formed four groups with different people's welfare indicators characteristics. The group that needs top priority in improvement is group 2 because it has a low level of people's welfare. Cluster 1 contains regencies/cities with high people's welfare characteristics in the housing and employment indicators. Cluster 3 includes regencies/municipalities with high people's welfare characteristics in the consumption pattern level, poverty, employment, and health indicators. Cluster 4 contains cities with high people's welfare characteristics in education and population indicators.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2024-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12485</dc:identifier>
	<dc:identifier>10.30598/barekengvol18iss3pp1839-1852</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application; 1839-1852</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol18iss3year2024</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12485/8535</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Priscilla Ardine Puspitasari, Defi Yusti Faidah, Triyani Hendrawati</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>