Pemodelan Angka Kematian Bayi di Indonesia menggunakan Geographically Weighted Regression

Arundati, Rasendriya (2025) Pemodelan Angka Kematian Bayi di Indonesia menggunakan Geographically Weighted Regression. Other thesis, Institut Teknologi Sepuluh Nopember.

Text 2043211059-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 April 2027. Download (4MB) | Request a copy

Abstract

Penurunan angka kematian bayi menjadi salah satu tujuan dalam Sustainable Development Goals (SDGs) yang ditunjukkan pada target mengakhiri semua kematian yang dapat dicegah di bawah usia 5 tahun. Angka kematian bayi di Indonesia pada tahun 2023 berada di angka 7,243 per 1.000 kelahiran hidup yang mengalami kenaikan dari tahun 2022 dengan angka kematian bayi sebesar 4,655 per 1.000 kelahiran hidup. Kondisi tersebut memerlukan perhatian lebih oleh pemerintah agar dilakukan tindakan penanganan atas kenaikan angka kematian bayi ini. Penelitian ini bertujuan untuk mengetahui faktor yang memengaruhi angka kematian bayi di Indonesia tahun 2023 pada setiap provinsi. Analisis dilakukan dengan memerhatikan aspek spasial yaitu setiap provinsi di Indonesia sehingga penelitian dilakukan dengan metode Geographically Weighted Regression (GWR) untuk memodelkan angka kematian bayi di Indonesia pada tahun 2023. Pemodelan dilakukan menggunakan fungsi pembobot Fixed Gaussian. Hasil pemodelan menunjukkan bahwa variabel prediktor yang berpengaruh signifikan terhadap angka kematian bayi pada hampir seluruh provinsi adalah persentase bayi berat lahir rendah (X1), yaitu signifikan pada 33 provinsi di Indonesia. Peningkatan persentase bayi berat lahir rendah berpengaruh signifikan terhadap peningkatan angka kematian bayi di Indonesia. Selain itu, variabel persentase ibu hamil mendapatkan K4 (X2) berpengaruh signifikan pada 15 provinsi, variabel Persentase Bayi Baru Lahir Mendapatkan Imunisasi Dasar Lengkap (IDL) (X4) berpengaruh signifikan pada 19 provinsi, dan variabel Persentase Penduduk Miskin (X5) berpengaruh signifikan terhadap angka kematian bayi di 18 provinsi. Model GWR yang dihasilkan memiliki kebaikan model sebesar 75,25% dengan nilai AIC model GWR sebesar 122,877.
====================================================================================================================================
The reduction of infant mortality is one of the goals in the Sustainable Development Goals (SDGs), as highlighted by the target to end all preventable deaths under the age of 5. In Indonesia, the infant mortality rate in 2023 stood at 7.243 per 1,000 live births, an increase from 2022, which recorded an infant mortality rate of 4.655 per 1,000 live births. This condition requires greater attention from the government to take measures to address the rise in infant mortality. This study aims to identify the factors influencing the infant mortality rate in Indonesia in 2023 for each province. The analysis considers the regional aspects which is each province in Indonesia using the Geographically Weighted Regression (GWR) method to model the infant mortality rate in Indonesia in 2023. The modeling was carried out using a Fixed Gaussian weighting function. The modeling results show that the predictor variable that has a significant effect on the infant mortality rate in almost all provinces is the percentage of low birth weight babies (X1), which is significant in 33 provinces in Indonesia. The increase in the percentage of low birth weight babies has a significant effect on the increase in infant mortality rates in Indonesia. In addition, the variable of the percentage of pregnant women getting K4 (X2) had a significant effect on 15 provinces, the variable Percentage of Newborns Getting Complete Basic Immunization (IDL) (X4) had a significant effect on 19 provinces, and the variable Percentage of Poor Population (X5) had a significant effect on the infant mortality rate in 18 provinces. The resulting GWR model has a model goodness of 75.25% with an AIC value of 122.877 GWR model.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Angka Kematian Bayi, Geographically Weighted Regression, Indonesia, Sustainable Development Goals, Geographically Weighted Regression, Indonesia, Infant Mortality Rate, Sustainable Development Goals
Subjects:	H Social Sciences > HA Statistics > HA30.6 Spatial analysis H Social Sciences > HA Statistics > HA31.3 Regression. Correlation. Logistic regression analysis.
Divisions:	Faculty of Vocational > 49501-Business Statistics
Depositing User:	Rasendriya Arundati
Date Deposited:	07 Feb 2025 02:32
Last Modified:	07 Feb 2025 02:32
URI:	http://repository.its.ac.id/id/eprint/118521

Actions (login required)

View Item