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Modeling Spatial Autoregressive Moving Average on the Human Development Index: (Studi Kasus: Indeks Pembangunan Manusia Kabupaten/Kota di Pulau Kalimantan)

MODEL SPATIAL AUTOREGRESSIVE MOVING AVERAGE DENGAN PEMBOBOT ROOK CONTIGUITY: (Studi Kasus: Indeks Pembangunan Manusia Kabupaten/Kota di Pulau Kalimantan)

Jurnal Matematika, Statistika dan Komputasi · Vol. 21 No. 3 (2025) · pp. 646–654

DOI
10.20956/j.v21i3.36392
ISSN
2614-8811, 1858-1382
Published
2025-05-14
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

English

The Spatial Autoregressive Moving Average (SARMA) model is a regression model developed from the classical regression model. The classical regression model produces inaccurate conclusions when used to model spatial data because the assumption of independent errors is not met. The SARMA model has advantages in modeling spatial data that has a spatial effect on the lag of the dependent variable and error. This study aims to obtain a SARMA model and factors that significantly influence the Human Development Index (HDI) of districts and cities in Kalimantan in 2022 based on the SARMA model. The spatial weighting used in SARMA modeling is rook contiguity. The results of the study show that the SARMA model can model the HDI of districts and cities in Kalimantan. The factors that significantly influence the HDI of districts and cities in Kalimantan are the poverty rate, the open unemployment rate, and the number of hospital facilities.

Bahasa Indonesia

Regresi spasial merupakan pengembangan dari model regresi linier dengan mempertimbangkan pengaruh faktor lokasi. Model regresi spasial yang digunakan adalah Spatial Autoregressive Moving Average (SARMA). Penelitian ini bertujuan mendapatkan model SARMA dan faktor-faktor yang berpengaruh signifikan terhadap Indeks Pembangunan Manusia (IPM) kabupaten/kota di Pulau Kalimantan Tahun 2022 berdasarkan model SARMA. Hasil penelitian menunjukkan bahwa terdapat autokorelasi spasial pada data IPM kabupaten /kota di Pulau Kalimantan yang diperoleh dari uji Indeks Moran’s. Berdasarkan Uji lagrange multiplier, data IPM kabupaten/kota di Pulau Kalimantan dapat dimodelkan dengan model SARMA. Faktor-faktor yang berpengaruh signifikan terhadap IPM kabupaten/kota di Pulau Kalimantan berdasarkan model SARMA adalah persentase penduduk miskin, tingkat pengangguran terbuka, dan jumlah fasilitas rumah sakit.

Keywords

Collaboration network of the authors

12 authors · 19 co-authorship links

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Co-authorOrganisationJoint articles
Memi Nor HayatiAffiliation unknown2
Hairi SeptiyanorAffiliation unknown1
Syaripuddin SyaripuddinAffiliation unknown1
Putri Sri AstutiAffiliation unknown1
Nur Annisa FitriAffiliation unknown1
Fidia Deny Tisna AmijayaAffiliation unknown1
Dwi Indra YunistyaAffiliation unknown1
Sri WahyuningsihAffiliation unknown1
Yusrian PalilingAffiliation 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 Spatial Autoregressive Moving Average  on the Human Development Index: (Studi Kasus: Indeks Pembangunan Manusia Kabupaten/Kota  di Pulau Kalimantan)</dc:title>
	<dc:title xml:lang="id">MODEL SPATIAL AUTOREGRESSIVE MOVING AVERAGE DENGAN PEMBOBOT ROOK CONTIGUITY: (Studi Kasus: Indeks Pembangunan Manusia Kabupaten/Kota  di Pulau Kalimantan)</dc:title>
	<dc:creator xml:lang="en">Wiranata, Ardiman</dc:creator>
	<dc:creator xml:lang="en">Fathurahman, M.</dc:creator>
	<dc:creator xml:lang="en">Goejantoro, Rito</dc:creator>
	<dc:subject xml:lang="en">spatial regression</dc:subject>
	<dc:subject xml:lang="en">SARMA</dc:subject>
	<dc:subject xml:lang="en">HDI</dc:subject>
	<dc:description xml:lang="en">The Spatial Autoregressive Moving Average (SARMA) model is a regression model developed from the classical regression model. The classical regression model produces inaccurate conclusions when used to model spatial data because the assumption of independent errors is not met. The SARMA model has advantages in modeling spatial data that has a spatial effect on the lag of the dependent variable and error. This study aims to obtain a SARMA model and factors that significantly influence the Human Development Index (HDI) of districts and cities in Kalimantan in 2022 based on the SARMA model. The spatial weighting used in SARMA modeling is rook contiguity. The results of the study show that the SARMA model can model the HDI of districts and cities in Kalimantan. The factors that significantly influence the HDI of districts and cities in Kalimantan are the poverty rate, the open unemployment rate, and the number of hospital facilities.</dc:description>
	<dc:description xml:lang="id">Regresi spasial merupakan pengembangan dari model regresi linier dengan mempertimbangkan pengaruh faktor lokasi. Model regresi spasial yang digunakan adalah Spatial Autoregressive Moving Average (SARMA). Penelitian ini bertujuan mendapatkan model SARMA dan faktor-faktor yang berpengaruh signifikan terhadap Indeks Pembangunan Manusia (IPM) kabupaten/kota di Pulau Kalimantan Tahun 2022 berdasarkan model SARMA. Hasil penelitian menunjukkan bahwa terdapat autokorelasi spasial pada data IPM kabupaten /kota di Pulau Kalimantan yang diperoleh dari uji Indeks Moran’s. Berdasarkan Uji lagrange multiplier, data IPM kabupaten/kota di Pulau Kalimantan dapat dimodelkan dengan model SARMA. Faktor-faktor yang berpengaruh signifikan terhadap IPM kabupaten/kota di Pulau Kalimantan berdasarkan model SARMA adalah persentase penduduk miskin, tingkat pengangguran terbuka, dan jumlah fasilitas rumah sakit.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2025-05-14</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/36392</dc:identifier>
	<dc:identifier>10.20956/j.v21i3.36392</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 21 No. 3 (2025): MAY 2025; 646-654</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/36392/12796</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Jurnal Matematika, Statistika dan Komputasi</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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