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Identifikasi Prediktor Jumlah Kasus Baru Tuberkolosis di Jawa Barat: Perbandingan Regresi Poisson, Binomial Negatif, dan Poisson terbobot Geografis

ESTIMASI: Journal of Statistics and Its Application · Vol. 7 No. 1 (2026) · hlm. 39–53

DOI
10.20956/ejsa.v7i1.34314
ISSN
2721-379X, 2721-3803
Terbit
2026-05-05
Penerbit
Hasanuddin University
Bahasa
en

Abstrak

Tuberculosis (TB) is still a serious problem for the world, including Indonesia as the third largest contributor of TB cases in the world. This study aims to analyze the factors that affect the number of new TB cases in West Java Province as the province with the most TB cases in Indonesia in 2022. The response variable used is the number of new TB cases in West Java Province in 2022, while the predictor variables used are population density, number of AIDS cases, poverty, and sanitation. Since the dependent variable comes from counting procedure, we conducted the analysis through three models, namely Poisson regression, negative binomial regression, and Geographically Weighted Poisson Regression (GWPR). We find that in the negative binomial method there was only one insignificant predictor variable, namely population density. Based on influential predictor variables, GWPR models in districts / cities in West Java can be separated into four groups. The best model to analyze the factors affecting new TB cases is the negative binomial regression model with an AIC of 487.76.

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12 penulis · 21 tautan kepenulisan bersama · 3 lintas organisasi

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  • Yogyakarta State University
  • Universidad de Colima
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Rekan penulisOrganisasiArtikel bersama
Heru SukocoYogyakarta State University2
Kismiantini KismiantiniYogyakarta State University1
Adi Cilik PierewanYogyakarta State University1
Osval Antonio Montesinos-LópezUniversidad de Colima1
Lusi HariniYogyakarta State University1
Ade Setyani Nurmara SariYogyakarta State University1
Khoirunnisa Puspa NegariYogyakarta State University1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/ESTIMASI/oai, pertama di-harvest 2 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Identifikasi Prediktor Jumlah Kasus Baru Tuberkolosis di Jawa Barat: Perbandingan Regresi Poisson, Binomial Negatif, dan Poisson terbobot Geografis</dc:title>
	<dc:creator xml:lang="en">Khalishah, Athayya Putri</dc:creator>
	<dc:creator xml:lang="en">Advani, Nadjma Maulidya</dc:creator>
	<dc:creator xml:lang="en">Syahputra, Fathur Rahman</dc:creator>
	<dc:creator xml:lang="en">Brilliant, Indira Ihnu</dc:creator>
	<dc:creator xml:lang="en">Setiawan, Ezra Putranda</dc:creator>
	<dc:subject xml:lang="en">TBC</dc:subject>
	<dc:subject xml:lang="en">GWPR</dc:subject>
	<dc:subject xml:lang="en">incidence</dc:subject>
	<dc:subject xml:lang="en">Negative Binomial Regression Model</dc:subject>
	<dc:subject xml:lang="en">West Java</dc:subject>
	<dc:description xml:lang="en">Tuberculosis (TB) is still a serious problem for the world, including Indonesia as the third largest contributor of TB cases in the world. This study aims to analyze the factors that affect the number of new TB cases in West Java Province as the province with the most TB cases in Indonesia in 2022. The response variable used is the number of new TB cases in West Java Province in 2022, while the predictor variables used are population density, number of AIDS cases, poverty, and sanitation. Since the dependent variable comes from counting procedure, we conducted the analysis through three models, namely Poisson regression, negative binomial regression, and Geographically Weighted Poisson Regression (GWPR). We find that in the negative binomial method there was only one insignificant predictor variable, namely population density. Based on influential predictor variables, GWPR models in districts / cities in West Java can be separated into four groups. The best model to analyze the factors affecting new TB cases is the negative binomial regression model with an AIC of 487.76.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2026-05-05</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/ESTIMASI/article/view/34314</dc:identifier>
	<dc:identifier>10.20956/ejsa.v7i1.34314</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 7, No. 1, Januari, 2026 : Estimasi; 39-53</dc:source>
	<dc:source>2721-379X</dc:source>
	<dc:source>2721-3803</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/34314/14377</dc:relation>
</oai_dc:dc>