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Modeling of Regency/City Unemployment Rates in Java Island Using Multilevel Binary Logistic Regression

Bahasa Inggris

Jurnal Matematika, Statistika dan Komputasi · Vol. 21 No. 1 (2024) · pp. 62–77

DOI
10.20956/j.v21i1.35584
ISSN
2614-8811, 1858-1382
Published
2024-09-15
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

English

Multilevel binary logistic regression analysis is a development of logistic regression for hierarchical data structures. Hierarchical data is data from a population that has levels. This research examines the relationship model of Life Expectancy, Mean Years of Schooling, Expected Years of Schooling, Regency/City Minimum Wage as explanatory variables at level 1 (Regency) and Gross Regional Domestic Income (GRDP) as an explanatory variable at level 2 (Provincial) against Unemployment Rate (UR) as a response variable. The research results show that Life Expectancy and Minimum Wage at level 1 and GRDP at level 2 have a significant influence on district/city TPT on Java Island in 2022

Bahasa Indonesia

Analisis regresi logistik biner multilevel merupakan pengembangan dari regresi logistik untuk struktur data berhirarki. Data berhirarki adalah data dari populasi yang memiliki tingkatan (level). Penelitian ini mengkaji model hubungan Angka Harapan Hidup (AHH), Rata-Rata Lama Sekolah (RLS), Harapan Lama Sekolah (HLS), Upah Minimum Kabupaten/kota (UMK) sebagai peubah penjelas pada level 1 (Kabupaten) dan Pendapatan Domestik Regional Bruto (PDRB) sebagai peubah penjelas pada level 2 (Provinsi) terhadap Tingkat Pengangguran Terbuka (TPT) sebagai peubah respon. Hasil penelitian menunjukkan AHH dan UMK pada level 1 serta PDRB pada level 2 memberikan pengaruh signifikan terhadap TPT kabupaten/kota di Pulau Jawa tahun 2022.

Keywords

Collaboration network of the authors

40 authors · 121 co-authorship links · 4 across different organisations · showing the 40 most connected

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

  • IPB University
  • Bandung Islamic University
  • Binus University
  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Muhammad Nur AidiAffiliation unknown3
Kusman SadikAffiliation unknown3
Khairil Anwar NotodiputroBandung Islamic University3
Anwar FitriantoAffiliation unknown3
Aji Hamim WigenaAffiliation unknown2
Indahwati IndahwatiAffiliation unknown2
Erwan SetiawanAffiliation unknown2
Bagus SartonoIPB University2
Asysta Amalia PasaribuBinus University2
Budi SusetyoAffiliation unknown2
Zein Rizky SantosoAffiliation unknown1
Agus Mohamad SolehIPB University1
Maulida Fajrining TyasAffiliation unknown1
Christiana Anggraeni PutriAffiliation unknown1
Husnun NashirAffiliation unknown1
Irvanal HaqAffiliation unknown1
Efriwati EfriwatiAffiliation unknown1
Dwi Agustin Nuriani SirodjAffiliation unknown1
Yudi Fathul AminAffiliation unknown1
Gusti Arviana RahmanUniversitas Halu Oleo1
Arie PurwantoAffiliation unknown1
Muhammad Aziz SuprayogiAffiliation unknown1
Joao Ferreira Rendes Bean LolyAffiliation unknown1
Muhammad Azis SuprayogiAffiliation unknown1
I Made SumertajayaAffiliation unknown1
Agus Sofian Eka HidayatAffiliation unknown1
Dian KusumaningrumAffiliation unknown1
Siskarossa Ika OktoraAffiliation unknown1
Dariani MatualageAffiliation unknown1
Asysta Amalia PasaribuAffiliation unknown1
Nur Fitriyani SahamonyAffiliation unknown1
Hari WijayantoAffiliation unknown1
Ita WulandariAffiliation unknown1
Assyifa Lala Pratiwi HamidAffiliation unknown1
Pardomuan Robinson SihombingAffiliation 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://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Modeling of Regency/City Unemployment Rates in Java Island Using Multilevel Binary Logistic Regression</dc:title>
	<dc:title xml:lang="id">Bahasa Inggris</dc:title>
	<dc:creator xml:lang="en">Dewanti, Dhea</dc:creator>
	<dc:creator xml:lang="en">Kapiluka, Kristuisno Martsuyanto</dc:creator>
	<dc:creator xml:lang="en">Sembiring, Febryna</dc:creator>
	<dc:creator xml:lang="en">Alfira, Ajeng Bita</dc:creator>
	<dc:creator xml:lang="en">Kurnia, Anang</dc:creator>
	<dc:subject xml:lang="en">Unemployment Rate</dc:subject>
	<dc:subject xml:lang="en">Binary Logistic Regression</dc:subject>
	<dc:subject xml:lang="en">Multilevel</dc:subject>
	<dc:subject xml:lang="en">Hierarchical Data</dc:subject>
	<dc:subject xml:lang="id">Bahasa Inggris</dc:subject>
	<dc:description xml:lang="en">Multilevel binary logistic regression analysis is a development of logistic regression for hierarchical data structures. Hierarchical data is data from a population that has levels. This research examines the relationship model of Life Expectancy, Mean Years of Schooling, Expected Years of Schooling, Regency/City Minimum Wage as explanatory variables at level 1 (Regency) and Gross Regional Domestic Income (GRDP) as an explanatory variable at level 2 (Provincial) against Unemployment Rate (UR) as a response variable. The research results show that Life Expectancy and Minimum Wage at level 1 and GRDP at level 2 have a significant influence on district/city TPT on Java Island in 2022</dc:description>
	<dc:description xml:lang="id">Analisis regresi logistik biner multilevel merupakan pengembangan dari regresi logistik untuk struktur data berhirarki. Data berhirarki adalah data dari populasi yang memiliki tingkatan (level). Penelitian ini mengkaji model hubungan Angka Harapan Hidup (AHH), Rata-Rata Lama Sekolah (RLS), Harapan Lama Sekolah (HLS), Upah Minimum Kabupaten/kota (UMK) sebagai peubah penjelas pada level 1 (Kabupaten) dan Pendapatan Domestik Regional Bruto (PDRB) sebagai peubah penjelas pada level 2 (Provinsi) terhadap Tingkat Pengangguran Terbuka (TPT) sebagai peubah respon. Hasil penelitian menunjukkan AHH dan UMK pada level 1 serta PDRB pada level 2 memberikan pengaruh signifikan terhadap TPT kabupaten/kota di Pulau Jawa tahun 2022.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2024-09-15</dc:date>
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	<dc:identifier>10.20956/j.v21i1.35584</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 21 No. 1 (2024): SEPTEMBER 2024; 62-77</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/35584/11999</dc:relation>
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	<dc:rights xml:lang="en">Copyright (c) 2024 Jurnal Matematika, Statistika dan Komputasi</dc:rights>
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