Regional Prioritization for Free Nutritious Food Programs through Social Data Integration and Public Sentiment Analysis Using K-Means and NLP

Sanusi, Ratna Nur Mustika; Wijaya, Galih Kusuma; Harwanti, Nur Achmey Selgi

doi:10.15294/ujm.v13i2.25750

Jurnal

Regional Prioritization for Free Nutritious Food Programs through Social Data Integration and Public Sentiment Analysis Using K-Means and NLP

Unnes Journal of Mathematics · Vol. 14 No. 1 (2025) · hlm. 43–52

DOI: 10.15294/ujm.v13i2.25750
ISSN: 2460-5859, 2252-6943
Terbit: 2025-07-27
Penerbit: Universitas Negeri Semarang
Bahasa: eng

Abstrak

This study evaluates Indonesia's Free Nutritious Food Program (MBG) through an innovative dual-method approach combining geospatial clustering and sentiment analysis. Cluster analysis of 38 provinces identified three distinct priority zones: high-priority (Eastern Indonesia), medium-priority (Central Indonesia), and low-priority (Java-Bali-West Sumatra), revealing significant regional disparities. Parallel sentiment analysis of 1,358 social media posts showed 76.6% negative perceptions dominated by food safety concerns ("poisoning," "toxic"), contrasting with 23.4% positive feedback highlighting nutritional benefits. The study makes three key contributions: First, it demonstrates the disconnect between regional needs and implementation quality. Second, it introduces an integrated monitoring framework combining cluster mapping with real-time sentiment tracking. Third, it proposes actionable solutions including a rapid-response task force and targeted communication strategies. These findings provide policymakers with evidence-based tools to simultaneously address geographical inequities and improve program execution in nutrition interventions.

Kata kunci

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7 penulis · 11 tautan kepenulisan bersama · 2 lintas organisasi

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State University of Semarang
IPB University

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Tampilkan sebagai tabel

Rekan penulis	Organisasi	Artikel bersama
Alfian Adi Pratama	State University of Semarang	2
Putriaji Hendikawati	State University of Semarang	2
Utami Dyah Syafitri	IPB University	1
Budi Susetyo	IPB University	1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/ujm/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en">Regional Prioritization for Free Nutritious Food Programs through Social Data Integration and Public Sentiment Analysis Using K-Means and NLP</dc:title>
	<dc:creator xml:lang="en">Sanusi, Ratna Nur Mustika</dc:creator>
	<dc:creator xml:lang="en">Wijaya, Galih Kusuma </dc:creator>
	<dc:creator xml:lang="en">Harwanti, Nur Achmey Selgi </dc:creator>
	<dc:subject xml:lang="en">Clustering</dc:subject>
	<dc:subject xml:lang="en"> sentiment analysis</dc:subject>
	<dc:subject xml:lang="en">public health program evaluation</dc:subject>
	<dc:subject xml:lang="en">regional disparities</dc:subject>
	<dc:description xml:lang="en">This study evaluates Indonesia's Free Nutritious Food Program (MBG) through an innovative dual-method approach combining geospatial clustering and sentiment analysis. Cluster analysis of 38 provinces identified three distinct priority zones: high-priority (Eastern Indonesia), medium-priority (Central Indonesia), and low-priority (Java-Bali-West Sumatra), revealing significant regional disparities. Parallel sentiment analysis of 1,358 social media posts showed 76.6% negative perceptions dominated by food safety concerns ("poisoning," "toxic"), contrasting with 23.4% positive feedback highlighting nutritional benefits. The study makes three key contributions: First, it demonstrates the disconnect between regional needs and implementation quality. Second, it introduces an integrated monitoring framework combining cluster mapping with real-time sentiment tracking. Third, it proposes actionable solutions including a rapid-response task force and targeted communication strategies. These findings provide policymakers with evidence-based tools to simultaneously address geographical inequities and improve program execution in nutrition interventions.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-07-27</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/ujm/article/view/25750</dc:identifier>
	<dc:identifier>10.15294/ujm.v13i2.25750</dc:identifier>
	<dc:source xml:lang="en">Unnes Journal of Mathematics; Vol. 14 No. 1 (2025): Unnes Journal of Mathematics Volume 1, 2025; 43-52</dc:source>
	<dc:source>2460-5859</dc:source>
	<dc:source>2252-6943</dc:source>
	<dc:source>10.15294/ujm.v14i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/ujm/article/view/25750/5201</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Unnes Journal of Mathematics</dc:rights>
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