Topic Analysis of Indonesian Online News on the Free Nutritious Meal Program Using Non-Negative Matrix Factorization

Abstract

The Free Nutritious Meal Program (MBG) represents a key policy of the Indonesian government to address malnutrition and stunting by providing nutritious meals for students. This study applies Non-Negative Matrix Factorization (NMF) for topic modeling on a long-text corpus of 5,390 digital news articles collected from seven national portals, with the aim of mapping public discourse on MBG. The optimal number of topics was determined using the coherence score, yielding nine distinct themes. Findings indicate that media coverage primarily revolves around program distribution in schools, the role of Micro, Small, and Medium Enterprises (MSMEs) and the food sector, budget allocation, political dynamics of national figures, and health-related concerns such as student poisoning cases. The results suggest that MBG is widely perceived as a strategic policy with broad implications for public policy, economic development, political debate, and social welfare. Methodologically, this research demonstrates the effectiveness of NMF in identifying latent thematic structures within long-text news corpora, offering insights into how digital media frames and interprets government initiatives.

Keywords

Free Nutritious Meal Program, NMF, MBG, Online News, Topic Modeling

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References

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