Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal Jurnal Gaussian
Riszki Bella Primasari
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
PEMODELAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) DENGAN JARAK EUCLIDEAN DAN JARAK MANHATTAN (STUDI KASUS : KEMATIAN BAYI NEONATAL DI JAWA TENGAH TAHUN 2018-2020) Riszki Bella Primasari; Agus Rusgiyono; Dwi Ispriyanti
Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.11.4.478-487

Abstract

Neonatal is a condition of babies from birth to 28 days. Data on Indonesia's health profile in 2020 showed that 72% of the number of deaths of toddlers occurred during the neonatal period and Central Java became the highest province of cases. Factors that are suspected to influence are the number of low birth weight babies (X1), the number of obstetric complications (X2), the number of Puskesmas (X3), the number of Posyandu (X4), the number of exclusive breastfeeding babies 0-6 months (X5), the number of pediatricians (X6), the number of ambulance cars (X7). Linear regression modeling on the number of neonatal infant deaths in Central Java has a heteroskedasticity problem so that Geographically Weighted Regression (GWR) is used. The distances used are Euclidean and Manhattan as well as the weighting function using Exponential and Tricube Kernel with Fixed Bandwidth. GWR modeling shows that not all independent variables are local, so Mixed Geographically Weighted Regression (MGWR) is used. The results of the GWR analysis with both distances and the two variable weighting functions are not local, including X2, X5, and X7. MGWR distance Manhattan Fixed Tricube Kernel became the better model, as the AICC value was smaller.
Co-Authors Agus Rusgiyono Dwi Ispriyanti