…
comment: The repository administrator has not yet configured an RDF license.
primary topic: ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEJADIAN BALITA STUNTING DI INDONESIA MENGGUNAKAN REGRESI SEMIPARAMETRIK SPLINE

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEJADIAN BALITA STUNTING DI INDONESIA MENGGUNAKAN REGRESI SEMIPARAMETRIK SPLINE
type: Thesis
type: Article
abstract: ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEJADIAN BALITA STUNTING DI INDONESIA MENGGUNAKAN REGRESI SEMIPARAMETRIK SPLINE Sakia Yety Ocsiella*, Yasmin Erika Faridhan, Hagni Wijayanti FMIPA, Universitas Pakuan, Jl. Pakuan, RT.02/RW.06, Tegallega, Kecamatan Bogor Tengah, Kota Bogor, Jawa Barat 16129 e-mail: sakiayocsiella@gmail.com Abstract: This study aims to model the percentage of stunting and to analyze the factors influencing stunting prevalence in Indonesia. The explanatory variables considered include the percentage of animal-source complementary feeding (MPASI), child nutritional status (weight-for-age), access to improved sanitation, access to unimproved drinking water, and the percentage of pregnant women consuming iron supplementation (TTD). The scatter plots between the response and explanatory variables indicate nonlinear patterns; therefore, a semiparametric spline regression approach is employed. Semiparametric spline regression offers flexibility in capturing both linear and non-linear relationships through the use of knot points. In this study, models are constructed using one, two, three, four, and a combination of knot points. The optimal model is selected based on the minimum value of Generalized Cross Validation (GCV). The data used are secondary data obtained from the Ministry of Villages, Development of Disadvantaged Regions, and Transmigration, covering 38 provinces in Indonesia in 2023. The results show that the best model is achieved using a combination of knot points (2,3,4), with a minimum GCV value of 30.5424 and a coefficient of determination (R²) of 89.42%. The findings indicate that all explanatory variables significantly influence the percentage of stunting. In conclusion, the semiparametric spline regression model is effective in modeling stunting prevalence in Indonesia and provides a flexible approach to capturing complex relationships between variables. Keywords: stunting, semiparametric regression, spline, GCV, explanatory variable…
date: 2025
status: published
author list: authors
type: EPrint
type: ThesisEPrint
is part of: repository
subject: Matematika

Universitas Pakuan
type: Organization

Fakultas Matematika dan Pengetahuan Alam, Universitas Pakuan
type: Organization

Yasmin Erika
type: Person
given name: Yasmin
family name: Erika

Hagni Wijayanti
type: Person
given name: Hagni
family name: Wijayanti

Saskia Yety Ocsiella
type: Person
given name: Saskia Yety
family name: Ocsiella

Fakultas Matematika dan Ilmu Pnegetahuan Alam
type: Organization

Program Studi Matematika
type: Organization

HTML Summary of #11067 ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEJADIAN BALITA STUNTING DI INDONESIA MENGGUNAKAN REGRESI SEMIPARAMETRIK SPLINE
format: text/html
primary topic: ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEJADIAN BALITA STUNTING DI INDONESIA MENGGUNAKAN REGRESI SEMIPARAMETRIK SPLINE

Matematika
type: Concept