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All Journal Unnes Journal of Mathematics
Harwanti, Nur Achmey Selgi
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Education Mathematics

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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
Unnes Journal of Mathematics Vol. 14 No. 1 (2025): Unnes Journal of Mathematics Volume 1, 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v13i2.25750

Abstract

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.
A Data Mining Approach to Wage Inequality Analysis in Indonesia: A Clustering Study Using Fuzzy C-Means Harwanti, Nur Achmey Selgi; Hendikawati, Putriaji; Sanusi, Ratna Nur Mustika; Pratama, Alfian Adi
Unnes Journal of Mathematics Vol. 13 No. 2 (2024): Unnes Journal of Mathematics Volume 2, 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v13i2.25755

Abstract

This study aims to cluster Indonesian provinces based on the average wage structure of workers across 17 economic sectors using the Fuzzy C-Means (FCM) method. The wage data underwent preprocessing steps including missing value imputation using the median, logarithmic transformation to reduce skewness, and Z-Score standardization to ensure uniform data scaling. The evaluation of the number of clusters and fuzziness values was conducted using the Silhouette coefficient and Fuzzy Partition Coefficient (FPC), with the best results achieved at three clusters and a fuzziness value of 1.3. Further analysis using Principal Component Analysis (PCA) provided visualization of the clusters, while radar charts illustrated wage characteristics by sector within each cluster. The clustering results reveal significant economic disparities among provinces: Cluster 1 consists of provinces with the highest wages dominated by high-value-added sectors such as mining and finance; Cluster 0 shows a balanced wage distribution reflecting a transitional economy; and Cluster 2 includes provinces with the lowest wages facing structural challenges. These findings offer a comprehensive overview of regional economic diversity in Indonesia and can serve as a basis for policy-making aimed at more equitable economic development.
Co-Authors Pratama, Alfian Adi Putriaji Hendikawati Sanusi, Ratna Nur Mustika Wijaya, Galih Kusuma