Geographically Weighted Poisson Regression Modeling Using Adaptive Gaussian Kernel Weighting For Mapping Maternal Mortality Rates In East Java

Ngoro, Inayati; Pramoedyo, Henny; Astuti, Ani Budi

doi:10.37905/jjbm.v6i4.30411

Akses terbuka Jurnal SINTA 1

Geographically Weighted Poisson Regression Modeling Using Adaptive Gaussian Kernel Weighting For Mapping Maternal Mortality Rates In East Java

Jambura Journal of Biomathematics (JJBM) · Vol. ume (2025) · hlm. 285–292

DOI: 10.37905/jjbm.v6i4.30411
ISSN: 2723-0317
Terbit: 2025-11-29
Penerbit: Department of Mathematics, Universitas Negeri Gorontalo
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-nc/4.0

Abstrak

Maternal Mortality Rate (MMR) is a key public health indicator that reﬂects spatial variation across districts in East Java. This study aims to model the spatial distribution of MMR using Geographically Weighted Poisson Regression (GWPR) with an Adaptive Gaussian Kernel weighting function. Secondary data were obtained from the 2022 East Java Provincial Health Proﬁle, covering 38 districts and municipalities. The results indicate that GWPR outperforms the classical Poisson regression. The intercept β=2.889 (exp=17.95) suggests an average of 18 maternal deaths in the absence of predictor effects. The coverage of the fourth antenatal care visit (K4) has a signiﬁcant negative effect ( β=-0.027; exp = 0.973), indicating that a 1% increase in K4 coverage reduces MMR by approximately 2.7%. Conversely, obstetric complications managed by midwives show a signiﬁcant positive effect (β= = 0.0173; exp = 1.017), meaning that a 1% increase in complications raises MMR by 1.7%. Other predictorsﬁrst antenatal care visit (K1), ironfolic acid (IFA) supplementation, and number of health workersare not statistically signiﬁcant. This study underscores the importance of expanding K4 coverage and strengthening complication management as priority strategies to reduce maternal mortality. Furthermore, GWPR-based mapping enables more targeted maternal health interventions tailored to local characteristics.

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University of Brawijaya
Universitas Negeri Surabaya
State University of Semarang
State University of Malang
University of Bengkulu
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Tampilkan sebagai tabel

Rekan penulis	Organisasi	Artikel bersama
Suci Astutik	University of Brawijaya	4
Wigbertus Ngabu	University of Brawijaya	4
Rahma Fitriani	University of Brawijaya	3
Atiek Iriany	University of Brawijaya	2
Achmad Efendi	University of Brawijaya	2
Rinda Lolita Melanwati	University of Brawijaya	1
Eni Sumarminingsih	University of Brawijaya	1
Adji Fernandes	Afiliasi tidak diketahui	1
Solimun	Afiliasi tidak diketahui	1
Dea Saraswati Pramaningrum	University of Brawijaya	1
Fachira Haneinanda Junianto	University of Brawijaya	1
Nabila Ayunda Sovia	Afiliasi tidak diketahui	1
Luthfatul Amaliana	Afiliasi tidak diketahui	1
Rossanda Sevia Gadis	Afiliasi tidak diketahui	1
Naurah Atikah Rabbani	Afiliasi tidak diketahui	1
Danang Ariyanto	Universitas Negeri Surabaya	1
Novi Nur Aini	University of Brawijaya	1
Sri Handayani	State University of Semarang	1
Nur Silviyah Rahmi	University of Brawijaya	1
Alifiandi Rafi Muhammad	University of Brawijaya	1
Ulfah Maisaroh	University of Brawijaya	1
Abdi Negara Guci	University of Brawijaya	1
Viky Iqbal Azizul Alim	State University of Malang	1
Laila Nur Azizah	University of Brawijaya	1
Meirida Karisma Putri	University of Brawijaya	1
Devi Azarina Manzilir Rohmah	University of Brawijaya	1
Fachri Faisal	University of Bengkulu	1
Sativandi Riza	University of Brawijaya	1
Agis Wahyu Lestari	University of Brawijaya	1
Suci Astutik urul Atiqah Romli	Afiliasi tidak diketahui	1
Agus Yarcana	University of Brawijaya	1
Elok Pratiwi	University of Brawijaya	1
Fahimah Fauwziyah	University of Brawijaya	1
Vita Dewi Islami	University of Brawijaya	1
Sri Andarini	University of Brawijaya	1
Muhammad Anismuslim	University of Brawijaya	1
Sudarto Sudarto	University of Brawijaya	1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ejurnal.ung.ac.id/index.php/JJBM/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en-US">Geographically Weighted Poisson Regression Modeling Using Adaptive Gaussian Kernel Weighting For Mapping Maternal Mortality Rates In East Java</dc:title>
	<dc:creator>Ngoro, Inayati</dc:creator>
	<dc:creator>Pramoedyo, Henny</dc:creator>
	<dc:creator>Astuti, Ani Budi</dc:creator>
	<dc:subject xml:lang="en-US">Maternal mortality rate; Geographically weighted Poisson regression; Adaptive Gaussian kernel</dc:subject>
	<dc:description xml:lang="en-US">Maternal Mortality Rate (MMR) is a key public health indicator that reﬂects spatial variation across districts in East Java.  This study aims to model the spatial distribution of MMR using Geographically Weighted Poisson Regression (GWPR) with an Adaptive Gaussian Kernel weighting function. Secondary data were obtained from the 2022 East Java Provincial Health Proﬁle, covering 38 districts and municipalities. The results indicate that GWPR outperforms the classical Poisson regression. The intercept β=2.889 (exp=17.95) suggests an average of 18 maternal deaths in the absence of predictor effects. The coverage of the fourth antenatal care visit (K4) has a signiﬁcant negative effect ( β=-0.027; exp = 0.973), indicating that a 1% increase in K4 coverage reduces MMR by approximately 2.7%. Conversely, obstetric complications managed by midwives show a signiﬁcant positive effect (β= = 0.0173; exp = 1.017), meaning that a 1% increase in complications raises MMR by 1.7%. Other predictorsﬁrst antenatal care visit (K1), ironfolic acid (IFA) supplementation, and number of health workersare not statistically signiﬁcant. This study underscores the importance of expanding K4 coverage and strengthening complication management as priority strategies to reduce maternal mortality.  Furthermore, GWPR-based mapping enables more targeted maternal health interventions tailored to local characteristics.</dc:description>
	<dc:publisher xml:lang="en-US">Department of Mathematics, Universitas Negeri Gorontalo</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-11-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ejurnal.ung.ac.id/index.php/JJBM/article/view/30411</dc:identifier>
	<dc:identifier>10.37905/jjbm.v6i4.30411</dc:identifier>
	<dc:source xml:lang="en-US">Jambura Journal of Biomathematics (JJBM); Volume 6, Issue 4: December 2025; 285-292</dc:source>
	<dc:source>2723-0317</dc:source>
	<dc:source>10.37905/jjbm.v6i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ejurnal.ung.ac.id/index.php/JJBM/article/view/30411/12082</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Inayati Ngoro, Henny Pramoedyo, Ani Budi Astuti</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-nc/4.0</dc:rights>
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