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A MODIFIED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODELING ON OPEN UNEMPLOYMENT RATE IN SOUTH SULAWESI

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 20 No. 2 (2026) · hlm. 1099–1110

DOI
10.30598/barekengvol20iss2pp1099-1110
ISSN
2615-3017, 1978-7227
Terbit
2026-01-26
Penerbit
PATTIMURA UNIVERSITY
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The Open Unemployment Rate (OUR) in Indonesia is still a challenge despite a decline, namely 4.82% in February 2024 and around 7.2 million unemployed people. The main cause of the OUR is the imbalance between the number of the workforce and the availability of jobs. This issue is directly related to the Sustainable Development Goals (SDGs), especially Goal 8 which focuses on the creation of decent jobs and economic growth. South Sulawesi Province has experienced a spike in the OUR in the last five years, especially due to the Covid-19 pandemic which caused the poverty rate to decline to 6.31% in 2020. Along with economic recovery, this figure decreased to 4.19% in August 2024. Although low, the thickness of the layer remains a concern because 4 out of 100 people have not been absorbed in the labor market. Therefore, it is important to identify the factors that influence the OUR in South Sulawesi in order to design a reduction strategy. Various factors that influence the OUR include the human development index, percentage of poor people, average length of schooling, life expectancy, population density, and regional gross domestic product. To analyze the influence of these factors, this study uses the Geographically and Temporally Weighted Regression (GTWR) method which can capture spatial and temporal variations. Modifications are made using the Mahalanobis distance to consider inter-regional correlation and the Locally Compensated Ridge (LCR) approach to overcome high collinearity in the data. The data used comes from the Central Statistics Agency of South Sulawesi Province. Meanwhile, partial testing obtained each observation of the influencing factors varying from 2020 to 2023. In general, the factors that significantly influence the open poverty rate in South Sulawesi in 2020-2023 are the human development index, percentage of poor people, average length of schooling and life expectancy.

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  • Padjadjaran University
  • Institut Agama Islam Negeri Kendari
  • Universitas Halu Oleo
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Rekan penulisOrganisasiArtikel bersama
Anisa KalondengAfiliasi tidak diketahui5
Sitti SahrimanAfiliasi tidak diketahui4
Muhammad FadilAfiliasi tidak diketahui4
Erna Tri HerdianiAfiliasi tidak diketahui3
Sri Astuti ThamrinAfiliasi tidak diketahui3
Raupong RaupongAfiliasi tidak diketahui3
Andi Kresna JayaAfiliasi tidak diketahui3
Anna IslamiyatiAfiliasi tidak diketahui3
Nirwan IlyasAfiliasi tidak diketahui3
M. Zaky Hisyam GozhiAfiliasi tidak diketahui2
Vika Yugi KurniawanAfiliasi tidak diketahui2
Ade Safira SeptianyAfiliasi tidak diketahui2
Georgina M. TinungkiAfiliasi tidak diketahui2
Georgina Maria TinungkiAfiliasi tidak diketahui2
Nasrah SirajangAfiliasi tidak diketahui2
Dhita Hartanti OctaviaAfiliasi tidak diketahui2
Budi AyuningsihPadjadjaran University1
Iman HernamanPadjadjaran University1
Tidi DhalikaPadjadjaran University1
Destu Putra RosmawanAfiliasi tidak diketahui1
Dedi RahmatPadjadjaran University1
Ashadi L. DiabInstitut Agama Islam Negeri Kendari1
Andi Novita Mudriani DjaoeInstitut Agama Islam Negeri Kendari1
Ansharullah AnsharullahUniversitas Halu Oleo1
Abdu Rahman BacoAfiliasi tidak diketahui1
Alfiana AntohAfiliasi tidak diketahui1
Yundy HafizriandaAfiliasi tidak diketahui1
Vieri KoerniawanAfiliasi tidak diketahui1
Novilia JaoAfiliasi tidak diketahui1
Riska RasyidAfiliasi tidak diketahui1
Ika Pratiwi HayaAfiliasi tidak diketahui1
Hajratul Ashwad KAfiliasi tidak diketahui1
Afifah Mutiah NurdinAfiliasi tidak diketahui1
Muh. Indirwan ArfanAfiliasi tidak diketahui1

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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 5 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">A MODIFIED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODELING ON OPEN UNEMPLOYMENT RATE IN SOUTH SULAWESI</dc:title>
	<dc:creator>Siswanto, Siswanto</dc:creator>
	<dc:creator>Sunusi, Nurtiti</dc:creator>
	<dc:creator>Yunita, Andi Isna</dc:creator>
	<dc:creator>Davala, Muhammad Ridzky</dc:creator>
	<dc:creator>Baso, Andi M. Alfin</dc:creator>
	<dc:creator>Nurfadilah, Nurfadilah</dc:creator>
	<dc:description xml:lang="en-US">The Open Unemployment Rate (OUR) in Indonesia is still a challenge despite a decline, namely 4.82% in February 2024 and around 7.2 million unemployed people. The main cause of the OUR is the imbalance between the number of the workforce and the availability of jobs. This issue is directly related to the Sustainable Development Goals (SDGs), especially Goal 8 which focuses on the creation of decent jobs and economic growth. South Sulawesi Province has experienced a spike in the OUR in the last five years, especially due to the Covid-19 pandemic which caused the poverty rate to decline to 6.31% in 2020. Along with economic recovery, this figure decreased to 4.19% in August 2024. Although low, the thickness of the layer remains a concern because 4 out of 100 people have not been absorbed in the labor market. Therefore, it is important to identify the factors that influence the OUR in South Sulawesi in order to design a reduction strategy. Various factors that influence the OUR include the human development index, percentage of poor people, average length of schooling, life expectancy, population density, and regional gross domestic product. To analyze the influence of these factors, this study uses the Geographically and Temporally Weighted Regression (GTWR) method which can capture spatial and temporal variations. Modifications are made using the Mahalanobis distance to consider inter-regional correlation and the Locally Compensated Ridge (LCR) approach to overcome high collinearity in the data. The data used comes from the Central Statistics Agency of South Sulawesi Province. Meanwhile, partial testing obtained each observation of the influencing factors varying from 2020 to 2023. In general, the factors that significantly influence the open poverty rate in South Sulawesi in 2020-2023 are the human development index, percentage of poor people, average length of schooling and life expectancy.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2026-01-26</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/18783</dc:identifier>
	<dc:identifier>10.30598/barekengvol20iss2pp1099-1110</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application; 1099-1110</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol20iss2year2026</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/18783/12430</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Siswanto Siswanto, Nurtiti Sunusi, Andi Isna Yunita, Muhammad Ridzky Davala, Andi M. Alfin Baso, Nurfadilah Nurfadilah</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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