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Arifah, Amalia
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Analisis Kebijakan Makan Bergizi Gratis Prabowo-Gibran dan Dampaknya Terhadap Kesehatan dan Bidang Pendidikan Maliki, Alinda; Selvi, Nanda; Arifah, Amalia; Assa, Novel Stefly
Jurnal Ilmiah Wahana Pendidikan Vol 11 No 12.B (2025): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

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This study examines the policy of the Free Nutritious Food (MBG) program launched by the Prabowo-Gibran administration in October 2024, using a qualitative literature review methodology. The program targets 82.9 million beneficiaries across Indonesia with a budget allocation of IDR 71 trillion for the first year. The study examines three key dimensions: improvement of nutrition and health, educational performance and cognitive development, and socio-economic impact. The findings indicate significant potential to reduce the prevalence of malnutrition and enhance school participation and attendance. However, challenges arise regarding food safety management, fiscal sustainability, and quality control in 190 operational kitchens across various geographical contexts. This study reveals that a comprehensive life-cycle approach targeting pregnant women, breastfeeding mothers, toddlers, and school-aged children addresses the intergenerational malnutrition cycle more effectively than previous programs. The research findings indicate that school feeding program policies yield positive returns ranging from $4 to $16 for every dollar invested. The success of implementation depends on strengthening inter-ministerial coordination and robust monitoring mechanisms to ensure equitable distribution of resources across Indonesia.
Regulasi Ai Dalam Administrasi Negara: Antara Inovasi dan Etika Almira, Nadya Disa; Arifah, Amalia; Assa, Novel Stefly; Selvi, Nanda; Maliki, Alinda
Jurnal Ilmiah Wahana Pendidikan Vol 11 No 12.B (2025): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

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This research investigates artificial intelligence regulation in public administration through systematic qualitative analysis of literature examining governance frameworks, implementation experiences, and theoretical foundations relevant to government management of algorithmic systems. The study identifies significant gaps between regulatory aspirations and implementation realities, revealing that fewer than forty percent of AI governance mandates are verifiably implemented across federal agencies. Key findings reveal three interrelated governance challenges: transparency and accountability deficits preventing meaningful citizen oversight, organizational capacity barriers constraining implementation across government organizations, and tensions between innovation encouragement and protection of ethical principles. The research applies principal-agent theory, organizational learning frameworks, and public value theory to understand these governance challenges, concluding that effective AI regulation requires integrated approaches combining technical solutions with stakeholder engagement, organizational capacity investment, and democratic deliberation about public values. The study emphasizes that sustainable AI governance depends upon treating public value creation as the ultimate evaluation criterion and establishing governance mechanisms balancing innovation with meaningful constraint
Co-Authors Almira, Nadya Disa Assa, Novel Stefly Maliki, Alinda Selvi, Nanda