[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120418-en":3,"doc-seo-120418-105":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},120418,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Benchmarking machine learning algorithm for stunting risk prediction in Indonesia - Research report","Stunting, driven by poor nutrition, causes below-average height development and long-term risks including impaired intellectual ability, reduced learning potential, and higher likelihood of chronic disease. A data science and machine learning approach is applied to build stunting risk prediction models using the CRISP-DM framework and 1,561 IFLS records from Indonesia. Random undersampling and SMOTE oversampling address class imbalance while four classifiers are benchmarked; oversampling improves performance, with logistic regression achieving the best results.","Benchmarking machine learning algorithm for stunting risk  \nprediction in Indonesia  \nNadya Novalina, Ibrahim Amyas Aksar Tarigan, Fatimah Kayla Kameela, Mia Rizkinia  \nDepartment of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, Indonesia  \n\n| Article history:\u003Cbr>Received Jul 13, 2024 Revised Dec 16, 2024 Accepted Dec 25, 2024 | Stunting is a condition caused by poor nutrition that results in below-average height development, potentially leading to long-term effects such as intellectual disability, low learning abilities, and an increased risk of developing chronic diseases. One effort to reduce stunting is to apply a machine learning algorithm with a data science approach to develop risk prediction models based on factors in stunting. The study used the current cross industry standard process for data mining (CRISP-DM) framework to gain insight and analyzed 1561 records of data collected from the Indonesia family life survey (IFLS) for the prediction models. Two sampling methods, random undersampling, and oversampling synthetic minority oversampling technique (SMOTE), were employed and compared to overcome the data imbalance problem. Four machine learning classifier algorithms were trained and tested to determine the best-performing model. The experiment results showed that the algorithms yielded an average accuracy of more than 75% . Using the undersampling technique, the accuracy obtained by logistic regression, k-nearest neighbor (KNN), support vector classifier (SVC), and decision tree classifier were 95.21%, 78.91%, 92.97%, and 86.26% respectively. Meanwhile, the oversampling technique reached 96.17%, 88.50%, 93.29%, and 95.21%, respectively. Logistic regression emerges asthe best classification, with oversampling yielding superior performance.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Cross industry standard process\u003Cbr>for data mining Machine learning Stunting\u003Cbr>Synthetic minority oversampling technique Undersampling |  |\n\nCorresponding Author:  \nMia Rizkinia  \nDepartment of Electrical Engineering, Faculty of Engineering, Universitas Indonesia Kampus UI Depok, Kukusan, Beji, Depok, West Java 16424, Indonesia [Email: mia@ui.ac.id](Email: mia@ui.ac.id)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nStunting is a critical issue faced by many developing countries, including Indonesia, and it has significant long-term effects on the growth and development of children. According to the World Health Organization (WHO), stunting is defined as the condition where a child ’s height falls below two standard deviations (-2 SD) from the median standard of healthy child growth [1] . Its impact extends beyond the physical aspects, affecting children ’s cognitive development. It also reduces optimal educational performance, potentially diminishes intellectual and motor capacities, and influences economic and social aspects [2]-[4] .  \nThe WHO data from 2018-2020 reveals that Indonesia ranks second in the Southeast Asia region for the highest prevalence of stunting, with Timor-Leste in first place and the Philippines in third [3] . According to the Indonesian Health Survey (SKI), conducted in 2023, 21.5% of children under five years old in Indonesia suffer from stunting [5] . This indicates that 1 in 5 children in Indonesia is affected. However, this percentage still exceeds the WHO threshold, which recommends that the prevalence of stunting should be less than 20%[6] .  \nThe early stages of a child ’s development, particularly the first thousand days after birth, constitute a challenging period for growth and development [7], [8] . Stunting, a highly complex problem with contributing factors such as inadequate maternal nutrition, health conditions, suboptimal child-feeding practices, and recurring subclinical infections [1], is often linked to poverty. In areas with high poverty rates, parents frequently face challenges in meeting basic household needs","cbCaifyUsqZne3rF","https://ap.wps.com/l/cbCaifyUsqZne3rF","pdf",915768,1,12,"English","en",105,"# Abstract\n# Introduction\n## Background of stunting\n## Prevalence in Indonesia and contributing factors\n## Prior studies and research motivation\n# Related Work\n# Methodology\n## Data source and CRISP-DM process\n## Sampling strategies for imbalance\n## Classifiers and evaluation\n# Results and Discussion\n## Benchmarking performance\n# Conclusion","[{\"question\":\"What causes stunting and why is it a major concern?\",\"answer\":\"Stunting results from poor nutrition that leads to height development below average. It can cause long-term impacts such as impaired intellectual ability, lower learning capacity, and increased chronic disease risk.\"},{\"question\":\"How does the study handle the data imbalance problem?\",\"answer\":\"The study compares random undersampling with SMOTE oversampling synthetic minority technique to mitigate imbalance and improve model learning.\"},{\"question\":\"Which machine learning classifier performs best for stunting risk prediction?\",\"answer\":\"Logistic regression delivers the top classification performance when using oversampling, achieving 96.17% accuracy in the reported results.\"}]","Benchmarking machine learning algorithm for stunting risk prediction in Indonesia - Research report | PDF",1785729946,30,{"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},"benchmarking-machine-learning-algorithm-for-stunting-risk-prediction-in-indonesia-research-report","",{"@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/benchmarking-machine-learning-algorithm-for-stunting-risk-prediction-in-indonesia-research-report/120418/",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","2026-08-03",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 causes stunting and why is it a major concern?","Question",{"text":75,"@type":76},"Stunting results from poor nutrition that leads to height development below average. It can cause long-term impacts such as impaired intellectual ability, lower learning capacity, and increased chronic disease risk.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study handle the data imbalance problem?",{"text":80,"@type":76},"The study compares random undersampling with SMOTE oversampling synthetic minority technique to mitigate imbalance and improve model learning.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning classifier performs best for stunting risk prediction?",{"text":84,"@type":76},"Logistic regression delivers the top classification performance when using oversampling, achieving 96.17% accuracy in the reported results.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]