[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126381-en":3,"doc-seo-126381-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126381,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Global Research Trends and Map on Machine Learning Applications in Stunting Detection in Vulnerable Populations - A Bibliometric Analysis","Stunting and malnutrition remain major public health problems, especially in low-income and rural populations. Driven by increasing data availability and analytics in health, machine learning (ML) is used to identify, classify, and predict undernutrition-related conditions. This bibliometric analysis examines global ML research from 2019–2025, focusing on methods such as clustering, support vector machines (SVM), and random forests. A total of 417 Scopus-indexed publications were analyzed with Biblioshiny (R) to evaluate trends, themes, authors, journals, and thematic evolution, showing a growth rate of 10.72% and a focus on machine learning, spatial analysis, and stunting.","Journal of Information Systems and Informatics  \nVol. 7, No. 3, September 2025 e-ISSN: 2656-4882 p-ISSN: 2656-5935  \nDOI: 10.51519/journalisi.v7i3.1248 Published By DRPM-UBD  \nGlobal Research Trends and Map on Machine Learning Applications in Stunting Detection in Vulnerable Populations: A Bibliometric Analysis  \nOtong Saeful Bachri1, Catur Edi Widodo2, Oky Dwi Nurhayati3  \n1,2,3Postgraduate School, Diponegoro University, Semarang, Indonesia  \n1Informatic Engineering, University Muhadi Setiabudi, Brebes, Indonesia [Email:](Email:1 otongsaefulbachri@students.undip.ac.id)[1](Email:1 otongsaefulbachri@students.undip.ac.id)[ otongsaefulbachri@students.undip.ac.id](Email:1 otongsaefulbachri@students.undip.ac.id), [2](2 caturediwidodo@lecturer.undip.ac.id)[ caturediwidodo@lecturer.undip.ac.id](2 caturediwidodo@lecturer.undip.ac.id)  \nAbstract  \nStunting and malnutrition continue to be significant public health challenges, particularly in low-income and rural populations. With the growing reliance on data-driven strategies in public health, machine learning (ML) has emerged as a promising tool for identifying, classifying, and predicting conditions related to undernutrition. This study presents abibliometric analysis of global research from 2019 to 2025, focusing on the application of ML techniques—such as clustering, support vector machines (SVM), and random forest—in addressing malnutrition and stunting. A total of 417 Scopus-indexed publications were analyzed using Biblioshiny (R) to assess research trends, key themes, influential authors, prominent journals, and thematic evolution. The analysis reveals a consistent growth rate of 10.72% in publications, with notable contributions from China and other low-and middle-income countries. Keyword mapping highlights that “machine learning,” “spatial analysis,” and “stunting” are central to the research, although they remain areas for further development. Thematic evolution indicates a shift towards more integrated, context-aware approaches, with a growing focus on built environments and vulnerable populations. The study concludes that while ML holds significant promise for advancing decision-making in child health and nutrition, its impact will depend on continued methodological refinement and effective implementation within public health systems.  \nKeywords: Machine Learning, Stunting, Malnutrition, Public Health, Bibliometric Analysis  \n1. INTRODUCTION  \nMalnutrition and stunting continue to be among the most pressing public health challenges affecting children in developing countries [1], [2], [3] . These conditions, which are particularly widespread in low-income and rural areas, have significant implications for childhood morbidity, cognitive development, and long-term socioeconomic disadvantage [4], [5] . In recent years, the growing availability of health-related datasets, coupled with advancements in computational technology, has sparked the integration of machine learning (ML) techniques into child health research [6], [7], [8], [9]. ML offers powerful tools for  \n2671  \nThis work is licensed under a Creative Commons Attribution 4.0 International License.  \np-ISSN: 2656-5935 [http://journal-isi.org/index.php/isi](http://journal-isi.org/index.php/isi) e-ISSN: 2656-4882  \nuncovering hidden patterns, classifying risk groups, and predicting malnutrition outcomes with remarkable accuracy and scalability [10], [11], [12] .  \nA growing body of literature has explored the application of ML techniques—such as support vector machines (SVM), decision trees, clustering algorithms, and ensemble learning methods—to identify key risk factors associated with stunting and undernutrition [13], [14], [15] . These studies typically leverage data from national demographic and health surveys (DHS), satellite imagery, and administrative records to build predictive models and generate spatial risk maps [12], [13] . Additionally, clustering methods like K-means and hierarchical clustering have been uti","cbCaitWVbySkLGce","https://ap.wps.com/l/cbCaitWVbySkLGce","pdf",1590006,6,1,13,"English","en",105,"# Introduction\n## Research gap and study aims\n## Research questions\n# Methodology","[{\"question\":\"What is the main focus of this study on stunting detection?\",\"answer\":\"It maps global research trends and presents a bibliometric analysis of machine learning applications used in stunting detection in vulnerable populations from 2019 to 2025.\"},{\"question\":\"Which machine learning techniques are highlighted in the analysis?\",\"answer\":\"The abstract emphasizes clustering methods and models such as support vector machines (SVM) and random forest for addressing malnutrition and stunting.\"},{\"question\":\"How many Scopus-indexed publications were analyzed and what tools were used?\",\"answer\":\"The study analyzed 417 Scopus-indexed publications using Biblioshiny (R) to assess trends, themes, influential authors, journals, and thematic evolution.\"}]","Global Research Trends and Map on Machine Learning Applications in Stunting Detection in Vulnerable Populations - 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