Melola Discovery

← Melola Discovery

Jurnal SINTA 3

PENERAPAN METODE GEOGRAPHICALLY WEIGHTED PANEL REGRESSION (GWPR) PADA KASUS KEMISKINAN DI INDONESIA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 15 No. 2 (2021) · pp. 241–248

DOI
10.30598/barekengvol15iss2pp241-248
ISSN
2615-3017, 1978-7227
Published
2021-06-01
Publisher
PATTIMURA UNIVERSITY
Language
eng

Abstract

To analyze the factor affecting poverty during several periods by considering some geographical factors, we can use a geographically weighted panel regression (GWPR) method. GWPR is a combination of the geographically weighted regression (GWR) model and the panel regression model. The research conducts to identify the factors affecting the percentage of poor people in 34 provinces in Indonesia during 2015-2019. The results show that a suitable GWPR model is a fixed-effect model (FEM) with an exponential adaptive kernel function. Referring to the model, the province is divided into four groups based on variables having a significant effect on the percentage of poor people. That factors causing the poor people percentage in Indonesia are the poor people percentage aged above 15 years old and unemployment, the people percentage aged above 15 years old and employed in the agricultural sector, the literacy rate of the poor aged between 15 to 55 years old, and the life expectancy rate. Keywords: fixed effect model, exponential adaptive kernel.

Collaboration network of the authors

31 authors · 88 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Halu Oleo
  • Universitas Mercu Buana Yogyakarta
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Hendra PerdanaAffiliation unknown4
Neva SatyahadewiAffiliation unknown4
Wirda AndaniAffiliation unknown4
Evy SulistianingsihAffiliation unknown4
Nur'ainul Miftahul HudaAffiliation unknown2
Siti AprizkiyandariAffiliation unknown2
Dadan KusnandarAffiliation unknown2
Tiara Herlinda SariAffiliation unknown2
Fransiskus FranAffiliation unknown1
Lisa FikadilaAffiliation unknown1
Fauziah SafitriAffiliation unknown1
Hani Dwi RetnaniAffiliation unknown1
Inggriani Millennia TaralyAffiliation unknown1
Dinda TiaraAffiliation unknown1
Wiji UmiatiAffiliation unknown1
Naomi Nessyana DebatarajaAffiliation unknown1
Cristy Framedia CahyaniAffiliation unknown1
Suryani SuryaniUniversitas Halu Oleo1
Mariatul KiftiahAffiliation unknown1
Nover LaurenAffiliation unknown1
Della ZariaAffiliation unknown1
Hendri AgustonoAffiliation unknown1
Rifki PebriyandiAffiliation unknown1
Risky GunawanAffiliation unknown1
Cinta Priscillia MaharaniAffiliation unknown1
Muhammad FikriUniversitas Mercu Buana Yogyakarta1
Evy SulitianingsihAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">PENERAPAN METODE GEOGRAPHICALLY WEIGHTED PANEL REGRESSION (GWPR) PADA KASUS KEMISKINAN DI INDONESIA</dc:title>
	<dc:creator>Martha, Shantika</dc:creator>
	<dc:creator>Yundari, Yundari</dc:creator>
	<dc:creator>Rizki, Setyo Wira</dc:creator>
	<dc:creator>Tamtama, Ray</dc:creator>
	<dc:description xml:lang="en-US">To analyze the factor affecting poverty during several periods by considering some geographical factors, we can use a geographically weighted panel regression (GWPR) method. GWPR is a combination of the geographically weighted regression (GWR) model and the panel regression model. The research conducts to identify the factors affecting the percentage of poor people in 34 provinces in Indonesia during 2015-2019. The results show that a suitable GWPR model is a fixed-effect model (FEM) with an exponential adaptive kernel function. Referring to the model, the province is divided into four groups based on variables having a significant effect on the percentage of poor people. That factors causing the poor people percentage in Indonesia are the poor people percentage aged above 15 years old and unemployment, the people percentage aged above 15 years old and employed in the agricultural sector, the literacy rate of the poor aged between 15 to 55 years old, and the life expectancy rate.
Keywords: fixed effect model, exponential adaptive kernel.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2021-06-01</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/2750</dc:identifier>
	<dc:identifier>10.30598/barekengvol15iss2pp241-248</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 15 No 2 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan; 241-248</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol15iss2year2021</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/2750/3151</dc:relation>
</oai_dc:dc>