DOI: https://doi.org/10.14710/j.gauss.14.2.433-%20444

PEMODELAN JUMLAH KASUS KEMATIAN BAYI DAN IBU DI PROVINSI LAMPUNG MENGGUNAKAN BIVARIATE GENERALIZED POISSON REGRESSION

*Dewi Indra Setiawan  -  Department of Data Science, Institut Teknologi Sumatera, Jl. Terusan Ryacudu, Indonesia
Purhadi Purhadi orcid scopus  -  Department of Statistics, Institut Teknologi Sepuluh Nopember, Jl. Teknik Mesin No.175, Keputih, Kec. Sukolilo, Surabaya, Jawa Timur 60111, Indonesia
Open Access Copyright 2025 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Maternal and infant mortality are closely related, as the fetus receives nutrition from the mother through the placenta. Therefore, the mother's health condition during pregnancy directly impacts fetal development. Additionally, the mother's role in caring for the infant significantly affects the child's growth and survival. One of the goals of the Lampung Provincial Health Office’s Regional Medium-Term Development Plan (RPJMD) for 2020–2024 is to reduce maternal and infant mortality. The expected health target by the end of 2024 is to lower maternal deaths to 110 cases and infant deaths to 520 cases. This study employs the Bivariate Generalized Poisson Regression (BGPR) method to identify factors influencing maternal and infant mortality in Lampung Province in 2022. BGPR is suitable for handling overdispersed count data with two correlated response variables. Based on the AICc criterion, the best model includes all prediktor variables. The results show that the percentage of deliveries assisted by health professionals (X1) significantly affects maternal mortality, while both the percentage of deliveries by health professionals (X1) and the percentage of fourth antenatal care visits (K4) (X3) significantly affect infant mortality.

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Data Jumlah Kematian Bayi dan Ibu di Lampung
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Keywords: AICc, Bivariate Generalized Poisson Regression, Infant Mortality, Maternal Mortality, Overdispersion.

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