Melola Discovery

← Melola Discovery

Open access Journal

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

Indonesian

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

37 authors · 119 co-authorship links · 43 across different organisations

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

  • IPB University
  • Politeknik Statistika STIS
  • Universitas Suryakancana
  • Binus University
  • National Research and Innovation Agency
  • Bandung Islamic University
  • Universitas Mercu Buana Yogyakarta
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
INDAHWATI INDAHWATIIPB University2
Erwan SetiawanUniversitas Suryakancana2
Khairil Anwar NotodiputroIPB University2
Asysta Amalia PasaribuBinus University2
Anwar FitriantoIPB University2
Zein Rizky SantosoAffiliation unknown1
Aji Hamim WigenaIPB University1
Agus Mohamad SolehIPB University1
Maulida Fajrining TyasIPB University1
Christiana Anggraeni PutriPoliteknik Statistika STIS1
Husnun NashirIPB University1
Muhammad Nur AidiIPB University1
Irvanal HaqIPB University1
Efriwati EfriwatiNational Research and Innovation Agency1
Dwi Agustin Nuriani SirodjBandung Islamic University1
Kusman SadikIPB University1
Yudi Fathul AminIPB University1
Gusti Arviana RahmanUniversitas Halu Oleo1
Muhammad Aziz SuprayogiAffiliation unknown1
Arie PurwantoUniversitas Mercu Buana Yogyakarta1
Joao Ferreira Rendes Bean LolyNational University of East Timor1
Muhammad Azis SuprayogiIPB University1
Bagus SartonoIPB University1
I Made SumertajayaIPB University1
Agus Sofian Eka HidayatPresident University1
Dian KusumaningrumUniversitas Prasetiya Mulya1
Siskarossa Ika OktoraPoliteknik Statistika STIS1
Dariani MatualageIPB University1
Nur Fitriyani SahamonyIPB University1
Hari WijayantoIPB University1
Pardomuan Robinson SihombingBadan Pusat Statistik1
ERFIANI ERFIANIIPB University1

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 8 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">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>
	<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://journal.unhas.ac.id/index.php/jmsk/article/view/35584</dc:identifier>
	<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>
	<dc:relation>10.20956/j.v21i1.35584.g11999</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Jurnal Matematika, Statistika dan Komputasi</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>