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Classification Of Country Status In 2022 Based On Social Indicators With Ordinal Logistic Regression

Jurnal Matematika, Statistika dan Komputasi · Vol. 20 No. 3 (2024) · hlm. 538–551

DOI
10.20956/j.v20i3.32356
ISSN
2614-8811, 1858-1382
Terbit
2024-05-15
Penerbit
Department of Mathematics, Hasanuddin University
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

This research examines the classification of country status in 2022 by applying ordinal logistic regression on various social indicators including education, health and economic. The urgency of the research is to know the country determine factors with specific factors in the form of research variables that can be useful for policy makers, unlike the existing classification which is only divided based on GDP per capita or HDI score only. By dividing 3 country status classes, namely not developed, developing and developed countries using the world bank classification baseline, the accuracy results were obtained at 72,5% but there were several variables that were not significant. After re-modelling, the accuracy was found increased to 76.4% with the odds ratio results for the minimum wage variable being 42,32 in the high class compared to the middle class and 11,66 for the middle class compared to the lower class, which means that the higher the minimum wage tends to be classify countries as developed countries. Another variable that has significance level is the birth rate with an odds ratio of 0,71 in the high and middle classes and 0.89 in the middle and lower classes comparison, which shows that this variable has a negative effect because the odds ratio is <1, which means that the higher the birth rate tends to make the country will be classified as a non-developed country.

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  • Universitas Airlangga
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Rekan penulisOrganisasiArtikel bersama
Nur ChamidahUniversitas Airlangga9
Dita AmeliaUniversitas Airlangga4
Galuh Cahya NugrahaUniversitas Airlangga3
Fatiha Nadia SalsabilaUniversitas Airlangga3
Rien RecyliaUniversitas Airlangga2
Najwa Khoir AldawiyahUniversitas Airlangga2
Nina AuliyahUniversitas Airlangga2
Mubadi'ul FitrianiUniversitas Airlangga2
Azizatul KholidiyahUniversitas Airlangga2
Fildzah Tri Januar ArianiUniversitas Airlangga2
Elly PusporaniUniversitas Airlangga2
Muhammad Fariz Fadillah MardiantoUniversitas Airlangga2
Gabriella Agnes BudijonoUniversitas Airlangga2
Azizah Atsariyyah ZhafiraUniversitas Airlangga2
Belindha Ayu ArdhaniUniversitas Airlangga1
Atikah FaizaUniversitas Airlangga1
Zidni Ilmatun NurrohmahUniversitas Airlangga1
Laili PuspasariUniversitas Airlangga1
Andini Sa’idahUniversitas Airlangga1
Nurin FaizunUniversitas Airlangga1
Kezia Eunike DarmawanUniversitas Airlangga1
Marfa Audilla FitriUniversitas Airlangga1
Nadhira Safa KamiilahUniversitas Airlangga1
Putu Eka AndrianiUniversitas Airlangga1
Cynthia Anggelyn SiburianUniversitas Airlangga1
Muhammad Rizaldy BaihaqiUniversitas Airlangga1
Ardi KurniawanUniversitas Airlangga1
Mochammad Bayu FirmansyahUniversitas Airlangga1
Sa’idah Zahrotul JannahUniversitas Airlangga1
Nicoletta Almira Dyah SetiawanUniversitas Airlangga1
Marisa RifadaUniversitas Airlangga1
Karina Rubita MakhbubahUniversitas Airlangga1
Devira Thania RamadhantyUniversitas Airlangga1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/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">Classification Of Country Status In 2022 Based On Social Indicators With Ordinal Logistic Regression </dc:title>
	<dc:creator xml:lang="en">Maula, Sugha Faiz Al </dc:creator>
	<dc:creator xml:lang="en">Nitasari, Alfi Nur </dc:creator>
	<dc:creator xml:lang="en">Putra, Mochamad Rasyid Aditya </dc:creator>
	<dc:creator xml:lang="en">Justin, Maelcardino Christopher </dc:creator>
	<dc:creator xml:lang="en">Sumarto, Salma Bethari Andjani </dc:creator>
	<dc:creator xml:lang="en">Suliyanto, Suliyanto</dc:creator>
	<dc:creator xml:lang="en">Saifudin, Toha </dc:creator>
	<dc:subject xml:lang="en">Country Classification</dc:subject>
	<dc:subject xml:lang="en">Ordinal Logistic Regression</dc:subject>
	<dc:subject xml:lang="en">Social Indicator</dc:subject>
	<dc:description xml:lang="en">This research examines the classification of country status in 2022 by applying ordinal logistic regression on various social indicators including education, health and economic. The urgency of the research is to know the country determine factors with specific factors in the form of research variables that can be useful for policy makers, unlike the existing classification which is only divided based on GDP per capita or HDI score only. By dividing 3 country status classes, namely not developed, developing and developed countries using the world bank classification baseline, the accuracy results were obtained at 72,5% but there were several variables that were not significant. After re-modelling, the accuracy was found increased to 76.4% with the odds ratio results for the minimum wage variable being 42,32 in the high class compared to the middle class and 11,66 for the middle class compared to the lower class, which means that the higher the minimum wage tends to be classify countries as developed countries. Another variable that has significance level is the birth rate with an odds ratio of 0,71 in the high and middle classes and 0.89 in the middle and lower classes comparison, which shows that this variable has a negative effect because the odds ratio is &amp;lt;1, which means that the higher the birth rate tends to make the country will be classified as a non-developed country.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2024-05-15</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/32356</dc:identifier>
	<dc:identifier>10.20956/j.v20i3.32356</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 20 No. 3 (2024): May 2024; 538-551</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/32356/11491</dc:relation>
	<dc:relation>10.20956/j.v20i3.32356.g11491</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Author and publisher</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>