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Kim, Yunho
Mathematical Imaging Analysis Lab.
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dc.citation.endPage 51555 -
dc.citation.startPage 51536 -
dc.citation.title IEEE ACCESS -
dc.citation.volume 12 - Caraka, Rezzy Eko - Supardi, Khairunnisa - Kaban, Puspita Anggraini - Kurniawan, Robert - Gio, Prana Ugiana - Kim, Yunho - Mufti, Syihabuddin Ahmad - Chen, Rung-Ching - Zuhanda, Muhammad Khahfi - Tyasti, Avia Enggar - Goldameir, Noor Ell - Pardamean, Bens - 2024-05-13T11:05:10Z - 2024-05-13T11:05:10Z - 2024-05-09 - 2024-03 -
dc.description.abstract This research endeavors to conduct a thorough investigation into pediatric health in the Papua and West Papua regions of Indonesia, employing a multifaceted approach that integrates data from various sources including RISKESDAS, SUSENAS, and remote sensing indicators such as NDVI (Normalized Difference Vegetation Index) and PDSI (Palmer Drought Severity Index). By amalgamating socio-economic data, health metrics, and environmental variables, the study seeks to achieve a comprehensive understanding of the factors influencing child health. Advanced analytical tools, notably the Generalized Linear Latent Variable Model (GLLVM), are utilized to conduct a dual-analysis of the intricate interplay between health indicators and environmental variables. The incorporation of NDVI and PDSI, derived from remote sensing data, introduces an ecological dimension to the investigation, facilitating a nuanced exploration of the impact of vegetation health and drought severity on pediatric health outcomes. This interdisciplinary research endeavors to unveil correlations, patterns, and potential causal relationships between environmental factors and the prevalence of various health issues among children, including pneumonia, acute respiratory infections, diarrhea, underweight, and stunting. The anticipated results aim to not only enrich our understanding of pediatric health in Papua and West Papua but also to contribute to broader discussions concerning the complex connections between health, socio-economic conditions, and environmental variables. By integrating remote sensing data with health and socio-economic datasets, this study provides a comprehensive perspective, enabling a more robust assessment of the determinants of child well-being. The insights gleaned from this research are poised to have significant implications for policymakers, environmental scientists, and public health professionals, informing the development of strategies aimed at enhancing child health outcomes in these regions. Furthermore, this study underscores the importance of initiatives like the Free Lunch and Milk Program proposed by Prabowo and Gibran in the 2024 Indonesian Presidential Election, emphasizing the critical role of holistic approaches in addressing the multifaceted challenges surrounding child health and well-being. -
dc.identifier.bibliographicCitation IEEE ACCESS, v.12, pp.51536 - 51555 -
dc.identifier.doi 10.1109/ACCESS.2024.3380018 -
dc.identifier.issn 2169-3536 -
dc.identifier.scopusid 2-s2.0-85188927467 -
dc.identifier.uri -
dc.identifier.wosid 001204956100001 -
dc.language 영어 -
dc.title Understanding Pediatric Health Trends in Papua: Insights From SUSENAS, RISKESDAS, Remote Sensing, and Its Relevance to Prabowo and Gibran's Free Lunch and Milk Program -
dc.type Article -
dc.description.isOpenAccess TRUE -
dc.relation.journalWebOfScienceCategory Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications -
dc.relation.journalResearchArea Computer Science; Engineering; Telecommunications -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor Pediatric health -
dc.subject.keywordAuthor RISKESDAS -
dc.subject.keywordAuthor SUSENAS -
dc.subject.keywordAuthor remote sensing -
dc.subject.keywordAuthor free lunch -
dc.subject.keywordAuthor milk program -
dc.subject.keywordAuthor socio-economic factors -
dc.subject.keywordPlus CHILD MALNUTRITION -
dc.subject.keywordPlus INDONESIA -
dc.subject.keywordPlus FAMILIES -
dc.subject.keywordPlus MODEL -


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