[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-407820-105":3,"detail-sidebar-cat-0-en-105":80,"doc-detail-407820-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":73,"head_meta":75,"extra_data":77,"updated_unix":79},105,"en","integrating-education-based-interventions-and-machine-learning-for-stunting-prevention-a-case-study-in-east-lombok-indonesia","Integrating education-based interventions and machine learning for stunting prevention: A case study in East Lombok, Indonesia","","Stunting prevention is examined in East Lombok, Indonesia, where West Nusa Tenggara shows a stunting burden close to 12.7% and is targeted for reduction by 2022. The study focuses on Puskesmas Denggen across six working areas, using 3416 under-five records collected in February 2024 and August 2024. A multidisciplinary intervention framework covering Motivation, Hygiene, Nutrition, Mental Health, and Infant Health is implemented alongside machine-learning models to predict future cases and evaluate effectiveness.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/integrating-education-based-interventions-and-machine-learning-for-stunting-prevention-a-case-study-in-east-lombok-indonesia/407820/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/integrating-education-based-interventions-and-machine-learning-for-stunting-prevention-a-case-study-in-east-lombok-indonesia/407820.png","ImageObject",300,407,{"name":42,"@type":43},"Cipher","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-28",true,{"@type":52,"interactionType":53,"userInteractionCount":33},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"What region and data source were used for the stunting prevention study?","Question",{"text":62,"@type":63},"The study was conducted in East Lombok, Indonesia, targeting Puskesmas Denggen and six working areas (Denggen, East Dengen, Majidi, Rakam, Sekarteja, and Pancor). It used 3416 under-five data records collected in February 2024 and August 2024.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"What types of interventions were included in the education-based model?",{"text":67,"@type":63},"The interventions include Motivation, Hygiene, Nutrition, Mental Health, and Infant Health. They are designed to cover essential needs during children’s growth and development.",{"name":69,"@type":60,"acceptedAnswer":70},"Which machine learning algorithms were reported to achieve high accuracy?",{"text":71,"@type":63},"The study reports very high accuracy using Decision Tree and Gaussian Naive Bayes for the anti-stunting education model.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},407820,1791628899,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,105,109,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":107,"slug":108},7,40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},8,"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":106,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":33,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":117,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":143,"read_time":103,"preview_page_count":4},687208528416,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Dialogues in Health 8 (2026) 100264  \nContents lists available at ScienceDirect Dialogues in Health  \njournal [homepage:](homepage: www.elsevier.com/locate/dialog)[ www.elsevier.com/locate/dialog](homepage: www.elsevier.com/locate/dialog)  \n| Integrating education-based interventions and machine learning for stunting prevention: A case study in East Lombok, Indonesia |  |  | |\n| --- | --- | --- | --- |\n| Mhd. Lailan Arqama, Asno Azzawagama Firdausb,*, Ahmad Muslih Atmojo a, Ginanjar Zukhruf Saputric, Furizal d, Palahuddine, Retno Sirnopatif\u003Cbr>a Department of Islamic Education, Universitas Ahmad Dahlan, Yogyakarta 55166, Indonesia\u003Cbr>b Department of Computer Science, Universitas Qamarul Huda Badaruddin Bagu, Central Lombok 83562, Indonesia c Department of Pharmacy, Universitas Ahmad Dahlan, Yogyakarta 55166, Indonesia\u003Cbr>d Department of Informatics Engineering, Universitas Islam Riau, Pekanbaru 28284, Indonesia\u003Cbr>e Department of Public Administration Science, Universitas Muhammadiyah Mataram, Mataram 83115, Indonesia f Department of Islamic Education, IAI Qamarul Huda, Central Lombok 83562, Indonesia |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords: Machine learning East Lombok Health\u003Cbr>Stunting |  | One of the regions in Indonesia that has the highest prevalence of stunting cases is West Nusa Tenggara, with a percentage of cases almost reaching 12.7 %, even though this province is a priority target for stunting reduction by 2022. Specifically in the East Lombok region, this study took this location point because of the high number of stunting cases in West Nusa Tenggara. Puskesmas Denggen was the target of the study, covering six working areas namely Denggen, East Dengen, Majidi, Rakam, Sekarteja, and Pancor, with a total of 3416 under-five data. The data were obtained through two measurements: the initial in February 2024 and the final in August 2024. This research integrates a multidisciplinary approach, encompassing health and nutrition science, psychology, education, and religion, to create comprehensive interventions for stunting prevention and employs machine learning models to predict future cases. The interventions include Motivation, Hygiene, Nutrition, Mental Health, and Infant Health, which are designed to cover all the essential needs of children in the growth and development process. The results of the six villages measured showed that significant changes in data were obtained in Denggen Village when compared before and after the intervention. The results of measuring the effectiveness of the anti-stunting educational interventions were also found to be effective across the five key aspects, with several showing dominant and statistically significant improvements. The machine learning algorithms used also achieved very high accuracy using Decision Tree and Gaussian Naive Bayes. This anti-stunting education model can be applied to the same case in a wider scope by paying attention to several aspects as an evaluation. |  |\n\n1. Introduction  \nHelminth infections contribute to 10–20 % prevalence among preschool children worldwide. Damage to the integrity of the intestinal barrier and decreased gastric acid secretion make the gastrointestinal tract more susceptible to pathogens, including parasites such as helminths [1]. These infections are a major factor that increases the risk of stunting. Globally in 2020, it is estimated that 149 million children under the age of 5 will be stunted, with significant geographical variations across different regions of the world [2]. The prevalence of stunting in Southeast Asia in children under five years old reached 24.7  \n% in 2018. In Indonesia, the figure was 30.8 %, although it decreased to 27.7 % in 2019, the issue remains a serious concern [3]. Improved health is considered an important aspect of well-being, and child health is known to have a long-term impact on productivity in adulthood [4].  \nStunting results from chronic and recurrent undernutrit","cbCaioJ0Sy7AIDJb","https://ap.wps.com/l/cbCaioJ0Sy7AIDJb","pdf",4572327,"English","# Introduction\n## Stunting and related health risks\n## Intervention design and evaluation","[{\"question\":\"What region and data source were used for the stunting prevention study?\",\"answer\":\"The study was conducted in East Lombok, Indonesia, targeting Puskesmas Denggen and six working areas (Denggen, East Dengen, Majidi, Rakam, Sekarteja, and Pancor). It used 3416 under-five data records collected in February 2024 and August 2024.\"},{\"question\":\"What types of interventions were included in the education-based model?\",\"answer\":\"The interventions include Motivation, Hygiene, Nutrition, Mental Health, and Infant Health. They are designed to cover essential needs during children’s growth and development.\"},{\"question\":\"Which machine learning algorithms were reported to achieve high accuracy?\",\"answer\":\"The study reports very high accuracy using Decision Tree and Gaussian Naive Bayes for the anti-stunting education model.\"}]","Integrating education-based interventions and machine learning for stunting prevention: A case study in East Lombok, Indonesia | PDF",1790576279]