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All Journal Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Bunga Mardhotillah
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Humanities Education Languange, Linguistic, Communication & Media Mathematics Other

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Analisis dan Upaya Menekan Faktor-Faktor Penyebab Stunting Menggunakan Neural Network Regression Guna Mencapai SDGs di Provinsi Jambi: Penelitian Ramayani Nur Hadiati; Wanti Perinduri Sihotang; Ario Surya Trinata; Bunga Mardhotillah
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 3 No. 4 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 3 Nomor 4 (April 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v3i4.1229

Abstract

Stunting is a serious public health issue with long-term impacts on human resource quality and the achievement of the Sustainable Development Goals (SDGs), including in Jambi Province. This study aims to analyze the contributing factors of stunting using a Machine Learning approach, specifically the Backpropagation Neural Network Regression method. The data used were obtained from the Jambi Province Central Bureau of Statistics in 2021, with independent variables including the percentage of infants not exclusively breastfed, poor sanitation access, pneumonia cases among children under five, tuberculosis cases, and the percentage of the poor population. The dependent variable is the percentage of stunted children under five. The study found that the best architecture was achieved with a learning rate of 0.01 and a network structure of 3-8-4-1-11, producing the lowest values of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), at 0.598, 0.773, and 0.63, respectively. This model is capable of identifying hidden indicators (hidden layers) from each stunting factor, which can be used to design more effective policy interventions. The study concludes that the application of Machine Learning can be an innovative solution to support strategic decision-making in reducing stunting rates in Jambi Province.
Analisis Faktor-Faktor Penurunan Angka Kemiskinan di Provinsi Jambi dengan Regresi Linier Berganda: Penelitian Afif Kurnia Ramadhan; Puspa Hanaya Latifah Erjandsa; Selina Febiyanti Sitorus; Bunga Mardhotillah
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 1 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 1 (Juli 2025 -
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i1.2033

Abstract

Poverty in Indonesia remains a situation that needs attention because although the poverty rate has been gradually declining, it is still a concern for developing countries like Indonesia. One region in Indonesia with a high poverty rate is Jambi. According to BPS, poverty in Jambi reached 279.37 thousand people as of March 2022. The purpose of this scientific paper is to examine the influence of educational facilities and educators, social assistance from the government, and trading activities on reducing the poverty rate in Jambi Province using regression analysis. The data used includes the number of schools, the number of teachers, the number of social assistance recipients, the number of traders, and the number of cooperatives in each sub-district in three areas: Jambi City, Muaro Jambi Regency, and Batang Hari Regency. The type of research being undertaken is quantitative and the research method used is data analysis using multiplr regression analysis. The coefficient of determination for the formed regression model is 86.7%, and the regression model is highly significant simultaneously. Based on the results, 5 factors were tested that have a relationship with the reduction of poverty rates. The most influential factor among these 5 factors is social assistance. This can be seen from the P-Value obtained which is less than 0.05.
Co-Authors Afif Kurnia Ramadhan Ario Surya Trinata Puspa Hanaya Latifah Erjandsa Ramayani Nur Hadiati Selina Febiyanti Sitorus Wanti Perinduri Sihotang