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All Journal Jurnal Abdi Mas Adzkia Journal of Mathematics, Computation and Statistics (JMATHCOS)
Munawar, Muhammad Arfie
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Humanities Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Pencegahan stunting dengan pengolahan daun kelor menjadi puding oleh mahasiswa KKN 136 UINSU di Desa Tanjung Mulia Hanifah, Fathia; Nasution, Fina Safitri; Munawar, Muhammad Arfie; Aliza, Mutia; Syaharani, Namira; Ritonga, Predy Ady Rey; Syukriah, Syukriah
Jurnal Abdi Mas Adzkia Vol 5, No 1 (2024): Agustus- Desember 2024
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/adzkia.v5i1.23721

Abstract

Pencegahan stunting merupakan salah satu prioritas utama pembangunan nasional di Indonesia, dengan tujuan menurunkan prevalensi stunting menjadi 14% pada tahun 2024. Penelitian dengan metode Deskriptif Kuantitatif, penelitian ini bertujuan untuk meningkatkan pengetahuan masyarakat, khususnya ibu hamil dan ibu yang memiliki balita, mengenai manfaat daun kelor dalam mencegah stunting. Sampel yang digunakan dalam penelitian ini adalah 16 ibu bayi balita. Penelitian pengabdian masyarakat yang dilakukan oleh mahasiswa KKN 136 UINSU di Desa Tanjung Mulia meliputi edukasi langsung melalui penyebaran poster, diskusi, dan demonstrasi pembuatan puding daun kelor. Hasil evaluasi menunjukkan bahwa 93,75% peserta memahami pengolahan daun kelor, dan 87,5% peserta mengetahui takaran konsumsi yang tepat. Sebelum edukasi, 10 dari 16 ibu balita tidak mengetahui manfaat daun kelor, namun setelah kegiatan, 15 dari 16 peserta memahami cara pengolahan dan manfaatnya. Kegiatan ini diharapkan dapat meningkatkan kesadaran masyarakat tentang pentingnya gizi yang baik untuk mencegah stunting pada anak.Stunting prevention is one of the national development priorities in Indonesia, with the aim of reducing the prevalence of stunting to 14% by 2024. Research using a Quantitative Descriptive method, this research aims to increase public knowledge, especially pregnant women and mothers with toddlers, regarding the benefits of Moringa leaves. in preventing stunting. The sample used in this research was 16 mothers of toddlers. Community service research carried out by KKN 136 UINSU students in Tanjung Mulia Village included direct education through distributing posters, discussions and presentations on making Moringa leaf pudding. The evaluation results showed that 93.75% of participants understood the processing of Moringa leaves, and 87.5% of participants knew the correct consumption dosage. Before the education, 10 out of 16 mothers of toddlers did not know the benefits of Moringa leaves, but after the activity, 15 out of 16 participants understood how to process it and its benefits. This activity is expected to increase public awareness about the importance of good nutrition to prevent stunting in children.
Geographically Weighted Regression Model in the Case of Unemployment in North Sumatra Munawar, Muhammad Arfie; Rahkmawati, Fibri; Nasution, Rini Halila
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/3vd63s64

Abstract

Unemployment is a major and complex issue that affects many aspects of society, particularly in regions such as North Sumatra. This issue is not merely about numbers it also concerns the welfare of the population. Each district or city exhibits varying levels of unemployment; some areas have high rates, while others are relatively low. These variations reflect a clear spatial heterogeneity. To address the significant spatial variation in the factors contributing to unemployment, this study applies the Geographically Weighted Regression (GWR) model to analyze and model unemployment in North Sumatra, taking into account the spatial variability of each independent variable’s influence. GWR is a regression method that allows model parameters to vary across geographic locations, making it possible to capture non-uniform relationships at different spatial points. The methodology involves four weighting functions adaptive Gaussian, adaptive bisquare, fixed Gaussian, and fixed bisquare to identify the most optimal model. The best-performing GWR model is then constructed, and the spatial distribution patterns of unemployment are analyzed. The data used in this study are sourced from official statistics. The results show that the adaptive bisquare GWR model provides the best performance, yielding the lowest AIC value of 130.066. Variables such as population density and population growth rate are significant in most regions. However, number of industries is only significant in certain areas, while total population and minimum wage are not significant. These findings indicate that the factors driving unemployment and their spatial distribution vary across regions, highlighting the importance of considering spatial heterogeneity.
Co-Authors Aliza, Mutia Hanifah, Fathia Nasution, Fina Safitri Nasution, Rini Halila Rahkmawati, Fibri Ritonga, Predy Ady Rey Syaharani, Namira Syukriah Syukriah, Syukriah