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All Journal JOURNAL SCIENTIFIC OF MANDALIKA (JSM) OKTAL : Jurnal Ilmu Komputer dan Sains
Anggun Yuliarum Qur’ani
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Humanities Astronomy Chemistry Computer Science & IT Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Estimasi Parameter Model Geographically Weighted Ridge Regression pada Indikator Pengukuran Penanganan Stunting di Indonesia Anggun Yuliarum Qur’ani; Made Ayu Dwi Octavanny; Ratna Sari Widiastuti
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 2 No 08 (2023): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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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.  
Pengelompokan Provinsi Di Indonesia Berdasarkan Indikator Kesehatan Balita Menggunakan Metode Agglomerative Clustering Ana Fikria; I Komang Gede Sukarsa; I Putu Winada Gautama; Made Ayu Dwi Octavanny; Anggun Yuliarum Qur’ani; Desak Putu Eka Nilakusmawati
Journal Scientific of Mandalika (JSM) e-ISSN 2745-5955 | p-ISSN 2809-0543 Vol. 7 No. 1 (2026)
Publisher : Institut Penelitian dan Pengembangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/10.36312/vol7iss1pp71-80

Abstract

Child Health is a crucial indicator in assessing the overall health of a population. However, there are disparities between provinces in terms of healthcare access, immunization coverage, and child nutrition status. Therefore, this study aims to cluster 38 provinces in Indonesia based on infant health indicators using the Agglomerative Hierarchical Clustering method. The data used is sourced from the 2023 Indonesian Health Profile Report, with variables including neonatal visit coverage, complete basic immunization, infant weighing, and the prevalence of infants with severe underweight, stunting, and malnutrition. The five agglomerative methods applied in this study are Single Linkage, Complete Linkage, Average Linkage, Centroid, and Ward. The results indicate disparities in child health conditions across provinces, with clusters representing regions with good, moderate, and poor conditions. These findings can serve as a reference for the implementation of the Free Nutritious Meal Program (MBG) in 2025 to better target areas with high vulnerability, in order to reduce stunting rates and improve overall child nutritional status.
Co-Authors Ana Fikria Desak Putu Eka Nilakusmawati I Komang Gede Sukarsa I Putu Winada Gautama MADE AYU DWI OCTAVANNY Ratna Sari Widiastuti