The Comparison of Kernel Weighting Functions in Geographically Weighted Logistic Regression in Modeling Poverty in Indonesia

Aliu, Muftih Alwi; Zubedi, Fahrezal; Yahya, Lailany; Oroh, Franky Alfrits

doi:10.20956/j.v18i3.19567

Open access Jurnal

The Comparison of Kernel Weighting Functions in Geographically Weighted Logistic Regression in Modeling Poverty in Indonesia

Perbandingan Fungsi Pembobot Kernel pada Geographically Weighted Logistic Regression dalam Memodelkan Kasus Kemiskinan di Indonesia

Jurnal Matematika, Statistika dan Komputasi · Vol. 18 No. 3 (2022) · pp. 362–384

DOI: 10.20956/j.v18i3.19567
ISSN: 2614-8811, 1858-1382
Published: 2022-05-15
Publisher: Department of Mathematics, Hasanuddin University
Language: en
License: creativecommons.org/licenses/by/4.0

Abstract

English

Indonesia is a developing country that is facing poverty. The percentage of the poor population in Indonesia in 2020 increased by 0.97 percent from 2019. A suitable analysis to overcome poverty in Indonesia is the regional effect, namely Geographically Weighted Logistic Regression (GWLR). This study aimed to compare the weighting functions of the Fixed Gaussian Kernel, Fixed Tricube Kernel, and Fixed Bisquare Kernel in the GWLR model in modeling poverty in Indonesia in 2020. The best model can determine significant factors that affected poverty in Indonesia in 2020. This study used the percentage data of poor population and the factors affecting it, namely the Open Unemployment Rate, Human Development Index, and Total Population in 34 Provinces in Indonesia. This study indicates that the GWLR model with the Fixed Gaussian Kernel weighting function is the best in modeling poverty in Indonesia in 2020 based on the smallest Akaike Information Criterion Corrected (AlCc) value. The GWLR model with the Fixed Gaussian Kernel weighting function shows the Open Unemployment Rate as a significant factor affecting poverty in Indonesia in 2020 in 10 provinces in Indonesia, namely Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, DKI Jakarta, and Banten.

Bahasa Indonesia

Indonesia merupakan negara berkembang yang dihadapkan pada masalah kemiskinan. Persentase penduduk miskin di Indonesia tahun 2020 meningkat 0.97 persen dari tahun 2019. Analisis yang cocok untuk mengatasi kemiskinan di Indonesia ini dengan menggunakan efek kewilayaan yaitu Geographically Weighted Logistic Regression (GWLR). Penelitian ini bertujuan untuk membandingkan fungsi pembobot Fixed Gaussian Kernel, Fixed Tricube Kernel dan Fixed Bisquare Kernel pada model GWLR dalam memodelkan kasus kemiskinan di Indonesia tahun 2020. Pada model terbaik dapat diketahui faktor signifikan yang mempengaruhi kemiskinan di Indonesia tahun 2020. Penelitian ini menggunakan data Persentase Penduduk Miskin dan faktor-faktor yang mempengaruhinya, yaitu Tingkat Pengangguran Terbuka, Indeks Pembangunan Manusia dan Jumlah Penduduk pada 34 Provinsi di Indonesia. Hasil penelitian ini yaitu model GWLR dengan fungsi pembobot Fixed Gaussian Kernel merupakan model terbaik dalam memodelkan kasus kemiskinan di Indonesia pada tahun 2020 yang diperoleh berdasarkan nilai Akeike Information Criterion Corrected (AICc) terkecil. Model GWLR dengan fungsi pembobot Fixed Gaussian Kernel menghasilkan Tingkat Pengangguran Terbuka merupakan faktor signifikan yang mempengaruhi kemiskinan di Indonesia tahun 2020 pada 10 Provinsi di Indonesia yaitu Provinsi Aceh, Sumatera Utara, Sumatera Barat, Riau, Jambi, Sumatera Selatan, Bengkulu, Lampung, DKI Jakarta dan Banten.

Keywords

Collaboration network of the authors

20 authors · 56 co-authorship links

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Co-author	Organisation	Joint articles
Agusyarif Rezka Nuha	Affiliation unknown	3
R. Resmawan	Affiliation unknown	2
Muhammad Rezky Friesta Payu	Affiliation unknown	2
Resmawan Resmawan	Affiliation unknown	1
Isran R. Olii	Affiliation unknown	1
Nurwan Nurwan	Affiliation unknown	1
Rahmayunda Usali	Affiliation unknown	1
Nur Safitri Abdul	Affiliation unknown	1
Sri Lestari Mahmud	Affiliation unknown	1
Nazrilla Hasan Laita	Affiliation unknown	1
Ismail Djakaria	Affiliation unknown	1
La Ode Nashar	Affiliation unknown	1
Amanda Adityaningrum	Affiliation unknown	1
Sri Indriani Ladjali	Affiliation unknown	1
Herlina Jusuf	Affiliation unknown	1
Melasarah Deswita Rahmadi	Affiliation unknown	1

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">The Comparison of Kernel Weighting Functions in Geographically Weighted Logistic Regression in Modeling Poverty in Indonesia</dc:title>
	<dc:title xml:lang="id">Perbandingan Fungsi Pembobot Kernel pada Geographically Weighted Logistic Regression dalam Memodelkan Kasus Kemiskinan di Indonesia </dc:title>
	<dc:creator xml:lang="en">Aliu, Muftih Alwi</dc:creator>
	<dc:creator xml:lang="en">Zubedi, Fahrezal</dc:creator>
	<dc:creator xml:lang="en">Yahya, Lailany</dc:creator>
	<dc:creator xml:lang="en">Oroh, Franky Alfrits</dc:creator>
	<dc:subject xml:lang="en">Fixed Gaussian Kernel</dc:subject>
	<dc:subject xml:lang="en">Fixed Tricube Kernel</dc:subject>
	<dc:subject xml:lang="en">Fixed Bisquare Kernel</dc:subject>
	<dc:subject xml:lang="en">GWLR</dc:subject>
	<dc:subject xml:lang="en">Poverty</dc:subject>
	<dc:subject xml:lang="id">Fixed Gaussian Kernel</dc:subject>
	<dc:subject xml:lang="id">Fixed Tricube Kernel</dc:subject>
	<dc:subject xml:lang="id">Fixed Bisquare Kernel</dc:subject>
	<dc:subject xml:lang="id">GWLR</dc:subject>
	<dc:subject xml:lang="id">Kemiskinan</dc:subject>
	<dc:description xml:lang="en">Indonesia is a developing country that is facing poverty. The percentage of the poor population in Indonesia in 2020 increased by 0.97 percent from 2019. A suitable analysis to overcome poverty in Indonesia is the regional effect, namely Geographically Weighted Logistic Regression (GWLR). This study aimed to compare the weighting functions of the Fixed Gaussian Kernel, Fixed Tricube Kernel, and Fixed Bisquare Kernel in the GWLR model in modeling poverty in Indonesia in 2020. The best model can determine significant factors that affected poverty in Indonesia in 2020. This study used the percentage data of poor population  and the factors affecting it, namely the Open Unemployment Rate , Human Development Index , and Total Population  in 34 Provinces in Indonesia. This study indicates that the GWLR model with the Fixed Gaussian Kernel weighting function is the best in modeling poverty in Indonesia in 2020 based on the smallest Akaike Information Criterion Corrected (AlCc) value. The GWLR model with the Fixed Gaussian Kernel weighting function shows the Open Unemployment Rate as a significant factor affecting poverty in Indonesia in 2020 in 10 provinces in Indonesia, namely Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, DKI Jakarta, and Banten.
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	<dc:description xml:lang="id">Indonesia merupakan negara berkembang yang dihadapkan pada masalah kemiskinan. Persentase penduduk miskin di Indonesia tahun 2020 meningkat 0.97 persen dari tahun 2019. Analisis yang cocok untuk mengatasi kemiskinan di Indonesia ini dengan menggunakan efek kewilayaan yaitu Geographically Weighted Logistic Regression (GWLR). Penelitian ini bertujuan untuk membandingkan fungsi pembobot Fixed Gaussian Kernel, Fixed Tricube Kernel dan Fixed Bisquare Kernel pada model GWLR dalam memodelkan kasus kemiskinan di Indonesia tahun 2020. Pada model terbaik dapat diketahui faktor signifikan yang mempengaruhi kemiskinan di Indonesia tahun 2020. Penelitian ini menggunakan data Persentase Penduduk Miskin &amp;nbsp;dan faktor-faktor yang mempengaruhinya, yaitu Tingkat Pengangguran Terbuka , Indeks Pembangunan Manusia &amp;nbsp;dan Jumlah Penduduk &amp;nbsp;pada 34 Provinsi di Indonesia. Hasil penelitian ini yaitu model GWLR dengan fungsi pembobot Fixed Gaussian Kernel merupakan model terbaik dalam memodelkan kasus kemiskinan di Indonesia pada tahun 2020 yang diperoleh berdasarkan nilai Akeike Information Criterion Corrected (AICc) terkecil. Model GWLR dengan fungsi pembobot Fixed Gaussian Kernel menghasilkan Tingkat Pengangguran Terbuka merupakan faktor signifikan yang mempengaruhi kemiskinan di Indonesia tahun 2020 pada 10 Provinsi di Indonesia yaitu Provinsi Aceh, Sumatera Utara, Sumatera Barat, Riau, Jambi, Sumatera Selatan, Bengkulu, Lampung, DKI Jakarta dan Banten.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2022-05-15</dc:date>
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	<dc:identifier>10.20956/j.v18i3.19567</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 18 No. 3 (2022): MAY, 2022; 362-384</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/19567/8139</dc:relation>
	<dc:relation>10.20956/j.v18i3.19567.g8139</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2022 Author and publisher</dc:rights>
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