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PENDEKATAN GEOGRAPHICALLY WEIGHTED ZERO INFLATED POISSON REGRESSION (GWZIPR) DENGAN PEMBOBOT FIXED BISQUARE KERNEL PADA KASUS DIFTERI DI INDONESIA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 14 No. 1 (2020) · pp. 039–046

DOI
10.30598/barekengvol14iss1pp039-046
ISSN
2615-3017, 1978-7227
Published
2020-03-01
Publisher
PATTIMURA UNIVERSITY
Language
eng

Abstract

The number of deaths due to diphtheria is counts data and there is a considerable presence of zeros (excess zeros). Besides, data on the spread of disease are generally geographically oriented or observed in each particular region, which is a type of spatial data. Geographically Weighted Zero Inflated Poisson Regression (GWZIPR), as the development of Geographically Weighted Regression (GWR) and Zero Inflated Poisson (ZIP) models will be used as a model in processing provincial diphtheria data in Indonesia in 2018, with the independent variable percentage of diphtheria cases (X1), percentage of vaccinated numbers (X2) and percentage of the population (X3) in each province in Indonesia. Estimating model parameters uses the method of maximum likelihood estimation. While the weighting function used is fixed bisquare kernel. Data is processed using software R packages lctools. The results were obtained if the model involved all three independent variables, the effect of the three independent variables on the number of deaths due to diphtheria was not significant. This is because there is a strong and significant relationship between independent variables, so that if the model does not involve a variable percentage of the population (population density), the percentage of vaccinated people affects the number of deaths caused by diphtheria significantly in an area. So that the provision of immunization vaccines can reduce the number of deaths caused by diphtheria

Collaboration network of the authors

40 authors · 115 co-authorship links · 2 across different organisations · showing the 40 most connected

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  • IPB University
  • Bandung Islamic University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Bagus SartonoIPB University4
Aji Hamim WigenaAffiliation unknown3
Erfiani ErfianiAffiliation unknown3
Anang KurniaAffiliation unknown3
Asep SaefuddinIPB University3
Indahwati IndahwatiAffiliation unknown2
Farit Mochamad AfendiAffiliation unknown2
Anggita Rizky FadilahAffiliation unknown2
Kusman SadikAffiliation unknown2
Khairil Anwar NotodiputroBandung Islamic University2
Lismayani UsmanAffiliation unknown1
Bayu Aji Eko PrasetyoAffiliation unknown1
Sim Hui XinAffiliation unknown1
Faula ArinaAffiliation unknown1
Utami SyafitriAffiliation unknown1
Andi Harismahyanti A.Affiliation unknown1
Embay RohaetiAffiliation unknown1
Farah AbqorunnisaAffiliation unknown1
Wiwik Andriyani Lestari NingsihAffiliation unknown1
Husnun NashirAffiliation unknown1
Ismail PranataAffiliation unknown1
Muhammad Nur AidiAffiliation unknown1
Nindi PigithaAffiliation unknown1
Ni Gusti Ayu Putu Puteri SuantariAffiliation unknown1
Agus Sofian Eka HidayatAffiliation unknown1
Dian KusumaningrumAffiliation unknown1
Nur HikmahAffiliation unknown1
I'lmisukma RisnawatiAffiliation unknown1
Hari WijayantoAffiliation unknown1
Kristuisno Martsuyanto KapilukaAffiliation unknown1
Agus Mohamad SolehIPB University1
Indra Rivaldi SiregarAffiliation unknown1
Ita WulandariAffiliation unknown1
Yunna Mentari IndahAffiliation unknown1
Lukmanul HakimAffiliation unknown1
Uswatun HasanahAffiliation 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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">PENDEKATAN GEOGRAPHICALLY WEIGHTED ZERO INFLATED POISSON REGRESSION (GWZIPR) DENGAN PEMBOBOT FIXED BISQUARE KERNEL PADA KASUS DIFTERI DI INDONESIA</dc:title>
	<dc:creator>Ismah, Ismah</dc:creator>
	<dc:creator>Sumertajaya, I Made</dc:creator>
	<dc:creator>Djuraidah, Anik</dc:creator>
	<dc:creator>Fitrianto, Anwar</dc:creator>
	<dc:description xml:lang="en-US">The number of deaths due to diphtheria is counts data and there is a considerable presence of zeros (excess zeros). Besides, data on the spread of disease are generally geographically oriented or observed in each particular region, which is a type of spatial data. Geographically Weighted Zero Inflated Poisson Regression (GWZIPR), as the development of Geographically Weighted Regression (GWR) and Zero Inflated Poisson (ZIP) models will be used as a model in processing provincial diphtheria data in Indonesia in 2018, with the independent variable percentage of diphtheria cases (X1), percentage of vaccinated numbers (X2) and percentage of the population (X3) in each province in Indonesia. Estimating model parameters uses the method of maximum likelihood estimation. While the weighting function used is fixed bisquare kernel. Data is processed using software R packages lctools. The results were obtained if the model involved all three independent variables, the effect of the three independent variables on the number of deaths due to diphtheria was not significant. This is because there is a strong and significant relationship between independent variables, so that if the model does not involve a variable percentage of the population (population density), the percentage of vaccinated people affects the number of deaths caused by diphtheria significantly in an area. So that the provision of immunization vaccines can reduce the number of deaths caused by diphtheria</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2020-03-01</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/1151</dc:identifier>
	<dc:identifier>10.30598/barekengvol14iss1pp039-046</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 14 No 1 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan; 039-046</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol14iss1year2020</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/1151/1422</dc:relation>
</oai_dc:dc>