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All Journal Unisda Journal of Mathematics and Computer Science (UJMC)
Ma'rifatul Julviana
Universitas Nahdlatul Ulama Blitar
Computer Science & IT Education Mathematics

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Analisis Faktor Acute Flaccid Paralysis Di Provinsi Jawa Timur Menggunakan Regresi Poisson Inverse Gaussian Ma'rifatul Julviana; Rizka Rizqi Robby; Galuh Tyasing Swastika; Ewing Rudita Arini
UJMC (Unisda Journal of Mathematics and Computer Science) Vol 11 No 2 (2025): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v11i2.10591

Abstract

Acute Flaccid Paralysis (AFP) is a paralysis condition that occurs suddenly and is weak, usually experienced by children under 15 years of age and is not caused by an accident. In 2005, Indonesia experienced its first case of the polio virus, when it almost received an international polio-free certificate. East Java Province, as one of the provinces with the largest population in Indonesia, has big challenges in controlling AFP cases. In this study there were 7 variables used including 1 dependent variable, namely the number of cases of Acute Flaccid Paralysis (AFP), and 6 independent variables including Population Density (), Percentage of Polio Immunization (), Number of Health Workers (), Percentage of Clean Water Availability (), Number of Poor Population (), and Human Development Index (HDI) (). In this research data, the variance value is much greater than the average (overdispersion), so to handle this, the Poisson inverse Gaussian regression method is used because it is very suitable for dealing with count data that experiences overdispersion. The best modeling form of Poisson inverse Gaussian regression for the number of AFP cases in East Java Province is as follows:. Based on hypothesis testing, the factors that have the most influence on the best model of AFP cases in East Java Province using Poisson inverse Gaussian (PIG) ​​regression are the Percentage of Polio Immunization (), and the Number of Health Workers (
Co-Authors EWING RUDITA ARINI Galuh Tyasing Swastika Rizka Rizqi Robby