Estimasi Parameter Model Geographically Weighted Ridge Regression pada Indikator Pengukuran Penanganan Stunting di Indonesia

Authors

  • Anggun Yuliarum Qur’ani Universitas Udayana
  • Made Ayu Dwi Octavanny Universitas Udayana
  • Ratna Sari Widiastuti Universitas Udayana

Keywords:

GWRR, Spatial Dependencies, Local Multicollinearity, Stunting, IKPS

Abstract

Regression that is implemented on cross-sectional data and weighted by geographic space coordinates is called Geographically Weighted Regression (GWR). When local multicollinearity problem is detected in GWR model, Geographically Weighted Ridge Regression (GWRR) can include this problem. One of the government's main agendas is to accelerate the reduction of stunting in children under five.. The prevalence of stunting among children under five shows a decrease between 2020 and 2021. GWRR performs very well in handling local multicollinearity problems by looking at the almost perfect coefficient of determination ( ) of 99.99815%. From all provinces in Indonesia, the biggest factors that influence IKPS are KPS/KKS or Food Aid Recipients, Education Dimension, and Immunization.  

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Additional Files

Published

20-08-2023

How to Cite

Anggun Yuliarum Qur’ani, Made Ayu Dwi Octavanny, & Ratna Sari Widiastuti. (2023). Estimasi Parameter Model Geographically Weighted Ridge Regression pada Indikator Pengukuran Penanganan Stunting di Indonesia. OKTAL : Jurnal Ilmu Komputer Dan Sains, 2(08), 2245–2253. Retrieved from https://journal.mediapublikasi.id/index.php/oktal/article/view/3450