Pemodelan Prevalensi Stunting di Indonesia Menggunakan Geographically Weighted Regression

Novitasari, Wahyu (2024) Pemodelan Prevalensi Stunting di Indonesia Menggunakan Geographically Weighted Regression. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Stunting atau kondisi gagal tumbuh pada anak di bawah lima tahun yang diakibatkan kekurangan gizi kronis merupakan salah satu target dalam pembangunan berkelanjutan atau Sustainable Development Goals (SDGs). Target dalam SDGs tersebut yaitu upaya penurunan stunting hingga 20% pada tahun 2025. Prevalensi stunting di Indonesia cukup tinggi karena masih di atas ambang batas yang ditetapkan oleh WHO sebesar 20%. Untuk menurunkan prevalensi stunting di Indonesia, maka perlu diidentifikasi faktor-faktor apa saja yang berpengaruh terhadap prevalensi stunting di Indonesia menggunakan Geographically Weighted Regression (GWR) yang mana metode GWR merupakan pengembangan dari regresi linear dan merupakan metode statistik yang memperhitungkan aspek spasial. Hasil dari analisis penelitian dapat digunakan untuk mengevaluasi serta merencanakan program preventif dalam upaya menurunkan prevalensi stunting di Indonesia. Berdasarkan hasil analisis, karakteristik prevalensi stunting di Indonesia tahun 2021 dan faktor-faktor yang mempengaruhinya menunjukkan pola yang cukup menyebar pada setiap provinsi. Penelitian ini menunjukkan bahwa terjadi peningkatan nilai R2 menjadi 95,11 persen pada model GWR dan penurunan nilai SSE menjadi 51,33 persen, sehingga pada kasus ini model GWR lebih baik dalam memodelkan apabila dibandingkan dengan model OLS. Perbedaan geografis berpengaruh terhadap prevalensi stunting di Indonesia, sehingga terbentuk model GWR yang berbeda-beda untuk setiap provinsi. Faktor persentase berat badan lahir rendah (BBLR) merupakan faktor yang berpengaruh dengan jumlah provinsi paling banyak.
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Stunting or failure to thrive in children under five years old due to chronic malnutrition is one of the targets in the Sustainable Development Goals (SDGs). The target is to reduce stunting by 20% by 2025. The prevalence of stunting in Indonesia is quite high because it is still above the threshold set by WHO of 20%. To reduce the prevalence of stunting in Indonesia, it is necessary to identify what factors affect the prevalence of stunting in Indonesia using Geographically Weighted Regression (GWR), which is a development of linear regression and is a statistical method that takes into account spatial aspects. The results of the research analysis can be used to evaluate and plan preventive programs in an effort to reduce the prevalence of stunting in Indonesia. Based on the results of the analysis, the characteristics of stunting prevalence in Indonesia in 2021 and the factors that influence it show a fairly dispersed pattern in each province. This study shows that there is an increase in the R2 value to 95,11 percent in the GWR model and a decrease in the SSE value to 51,33 percent, so in this case the GWR model is better at modeling when compared to the OLS model. Geographical differences affect the prevalence of stunting in Indonesia, so that different GWR models are formed for each province. The percentage of low birth weight (LBW) factor is an influential factor with the largest number of provinces.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Geographically Weighted Regression, Indonesia, Spatial, Stunting Prevalence, Sustainable Development Goals
Subjects:	H Social Sciences > HA Statistics H Social Sciences > HA Statistics > HA30.6 Spatial analysis H Social Sciences > HA Statistics > HA31.35 Analysis of variance H Social Sciences > HA Statistics > HA31.3 Regression. Correlation H Social Sciences > HA Statistics > HA31.7 Estimation Q Science Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression
Divisions:	Faculty of Vocational > 49501-Business Statistics
Depositing User:	Wahyu Novitasari
Date Deposited:	22 Feb 2024 01:03
Last Modified:	22 Feb 2024 01:05
URI:	http://repository.its.ac.id/id/eprint/107653

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