[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124296-en":3,"doc-seo-124296-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124296,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Stunting Prediction Modeling in Toddlers Using a Machine Learning Approach and Model Implementation for Mobile Application","Children’s health and development are essential for maintaining national productivity, and stunting remains a major malnutrition concern worldwide and in Indonesia. This study develops a stunting risk prediction model using public health data, transforming literature-identified determinants into model features. Three machine learning algorithms—XGBoost, Random Forest, and KNN—are trained and evaluated, then the best-performing model is deployed in a mobile application. Results show XGBoost achieves 85% accuracy, supporting early detection and intervention.","Accredited SINTA 2 Ranking  \nDecree of the Director General of Higher Education, Research, and Technology, No. 158/E/KPT/2021 Validity period from Volume 5 Number 2 of 2021 to Volume 10 Number 1 of 2026  \nPublished online at: [http://jurnal.iaii.or.id](http://jurnal.iaii.or.id)  \n\n|  | JURNAL RESTI\u003Cbr>(Rekayasa Sistem dan Teknologi Informasi)\u003Cbr>Vol. 9 No. 3 (2025) 670-676 e-ISSN: 2580-0760 |  |\n| --- | --- | --- |\n| Stunting Prediction Modeling in Toddlers Using a Machine Learning Approach and Model Implementation for Mobile Application\u003Cbr>Eko Abdul Goffar1,2*, Rosa Eliviani 1, Lili Ayu Wulandhari2\u003Cbr>1Departement of Informatics Management, Astra Polytechnic, Jakarta, Indonesia\u003Cbr>2Computer Science Departement, BINUS Graduate Program-Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480\u003Cbr>[1](1 eko.gofar@polytechnic.astra.ac.id)[ eko.gofar@polytechnic.astra.ac.id](1 eko.gofar@polytechnic.astra.ac.id), [1](1 rosa.eliviani@polytechnic.astra.ac.id)[ rosa.eliviani@polytechnic.astra.ac.id](1 rosa.eliviani@polytechnic.astra.ac.id), [2](2 lili.wulandhari@binus.ac.id)[ lili.wulandhari@binus.ac.id](2 lili.wulandhari@binus.ac.id)\u003Cbr>Abstract\u003Cbr>Children’s health and development are critical for maintaining national productivity and independence, with stunting being a major concern. Stunting, a form of malnutrition, impairs growth and development, affecting millions of people globally, including a significant number in Indonesia. This study addresses the challenge of stunting by developing a predictive model using machine learning techniques to forecast stunting risks based on public health data. The literature review section discusses the factors that influence stunting, and these factors are used as features to build a stunting prediction model. Then the features were used to build a model with three machine learning algorithms Extreme Gradient Boosting (XGBoost), Random Forest, and K-Nearest Neighbor (KNN) to build and evaluate models that predict stunting. The models were trained and assessed using public datasets and the most effective algorithm was integrated into a mobile application for practical use. The results indicate that the XGBoost model outperforms the other models with an accuracy of 85%, making it the optimal choice for implementation in a mobile application. The next-best model is selected to be implemented through a mobile application so that users can directly use the model that has been built. This application aims to enhance early detection and intervention efforts for stunting, potentially improving child health outcomes and contributing to long-term productivity by building predictive models and implementing the models into a mobile application. This study contributes to the implementation of models built using public data for application in mobile applications.\u003Cbr>Keywords: machine learning; mobile application; stunting prediction |  |  |\n| How to Cite: E. Abdul Goffar, R. Eliviani, and L. Ayu Wulandhari,“Stunting Prediction Modeling in Toddlers Using a Machine Learning Approach and Model Implementation for Mobile Application”, J. RESTI (Rekayasa Sist. Teknol. Inf.) , vol. 9, no. 3, pp. 670-676, Jun. 2025. Permalink/DOI: [https://doi.org/10.29207/resti.v9i3.6450](https://doi.org/10.29207/resti.v9i3.6450) |  |  |\n| Received: March 7, 2025\u003Cbr>Accepted: May 26, 2025\u003Cbr>Available Online: June 22, 2025 |  | This is an open-access article under the CC BY 4.0 License Published by Ikatan Ahli Informatika Indonesia |\n\n1. Introduction  \nChildren represent the next generation tasked with preserving national independence and enhancing future productivity. Therefore, it is crucial for society to ensure that children’s developing bodies are properly nurtured to support their growth according to their age. A prevalent issue today is stunting, a form of malnutrition that impairs children's growth and development, often evidenced by a height or length below standard norms. Stunting, a condition in whi","cbCaiuZxiZSHlsiw","https://ap.wps.com/l/cbCaiuZxiZSHlsiw","pdf",416164,1,7,"English","en",105,"# Introduction\n## Background of stunting and its impact\n## Determinants used as features\n## Study objective and machine learning approach","[{\"question\":\"What problem does the study address and why is it important?\",\"answer\":\"The study addresses stunting, a form of malnutrition that limits children’s growth and development. It is important because early prevention and detection can support better long-term child health and productivity.\"},{\"question\":\"Which machine learning algorithms are compared in the study?\",\"answer\":\"The study builds and evaluates three models using Extreme Gradient Boosting (XGBoost), Random Forest, and K-Nearest Neighbor (KNN).\"},{\"question\":\"What model performs best and how is it used?\",\"answer\":\"The XGBoost model outperforms the others with 85% accuracy. The best model is integrated into a mobile application to enable practical stunting risk prediction for users.\"}]","Stunting Prediction Modeling in Toddlers Using a Machine Learning Approach and Model Implementation for Mobile Application | PDF",1785821438,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"stunting-prediction-modeling-in-toddlers-using-a-machine-learning-approach-and-model-implementation-for-mobile-application","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/stunting-prediction-modeling-in-toddlers-using-a-machine-learning-approach-and-model-implementation-for-mobile-application/124296/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address and why is it important?","Question",{"text":75,"@type":76},"The study addresses stunting, a form of malnutrition that limits children’s growth and development. It is important because early prevention and detection can support better long-term child health and productivity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are compared in the study?",{"text":80,"@type":76},"The study builds and evaluates three models using Extreme Gradient Boosting (XGBoost), Random Forest, and K-Nearest Neighbor (KNN).",{"name":82,"@type":73,"acceptedAnswer":83},"What model performs best and how is it used?",{"text":84,"@type":76},"The XGBoost model outperforms the others with 85% accuracy. 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