[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120192-en":3,"doc-seo-120192-105":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},120192,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Determining Toddler’s Nutritional Status with Machine Learning Classification Analysis Approach","Toddler nutritional status is a widespread global concern, with malnutrition commonly requiring accurate identification, classification, and prediction support. This study evaluates children’s nutritional status using a machine learning classification analysis approach to enhance system accuracy and support better decisions in stunting-focused health interventions. Classification performance is built with Naive Bayes, Support Vector Machine, and Multilayer Perceptron, and is optimized using gridsearchCV. The study uses 6,812 toddler records from a Health Center in Tangerang Regency; results report 88% accuracy via precision-based evaluation.","Matrik: Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer  \nVol. 24, No. 2, March 2025, pp. 235∼246 ISSN: 2476-9843, accredited by Kemenristekdikti, Decree No: 200/M/KPT/2020  \nDOI: 10.30812/matrik.v24i2.4092 ❒ 235  \n\n| Determining Toddler’s Nutritional Status with Machine Learning\u003Cbr>Classification Analysis Approach\u003Cbr>Taufik Hidayat 1 , Mohammad Ridwan 1 , Muhamad Fajrul Iqbal 1 , Sukisno 1 , Robby Rizky2 , William Eric Manongga3\u003Cbr>1Universitas Islam Syekh-Yusuf, Tangerang, Indonesia\u003Cbr>2Universitas Mathla’ul Anwar, Banten, Indonesia\u003Cbr>3Chaoyang University of Technology, Taichung City, Taiwan |  |\n| --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received May 29, 2024 Revised February 16, 2025 Accepted March 06, 2025\u003Cbr>Keywords:\u003Cbr>Analysis model; Classification; Machine learning; Nutritional status; Toddlers. | ABSTRACT\u003Cbr>The nutritional status of toddlers is a common issue many countries face worldwide. Various facts indicate that malnutrition is a primary focus for many researchers. Several efforts have been made to address this problem, including developing analytical models for identification, classification, and prediction. This study aims to evaluate the nutritional status of children by utilizing a classification analysis approach using Machine Learning. This research aims to improve the accuracy of the classification system and facilitate better decision-making in stunted toddlers, which is a priority, especially in the health sector. The Machine Learning classification analysis process will later utilize the performance of the Naive Bayes algorithm, the Support Vector Machine algorithm, and the Multilayer Perceptron algorithm. ML performance can be optimized using gridsearchCV to produce optimal classification analysis patterns. The data set of this study uses 6812 toddler data sourced from the Health Center at the Tangerang Regency Health Office. Based on the research presented, Machine Learning performance in analyzing nutritional status classification provides maximum results. The results are reported based on a precision level with an accuracy of 88% . The results of this analysis can also present a classification of nutritional status based on knowledge. This study can contribute to and update the analysis model in determining nutritional status. The results of this study can also provide benefits in handling nutritional status problems that occur in children.\u003Cbr>Copyright ©2025 The Authors.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| Corresponding Author:\u003Cbr>Taufik Hidayat, +6281398475099 .,\u003Cbr>Faculty of Engineering and Informatics Engineering,\u003Cbr>Universitas Islam Syekh-Yusuf, Tangerang, Indonesia,\u003Cbr>Email: [thidayat@unis.ac.id](thidayat@unis.ac.id). |  |\n| How to Cite:\u003Cbr>T. Hidayat, M. Ridwan, M. Iqbal, S. Sukisno, R. Rizky, and W. Manongga,”Determining Toddler’s Nutritional Status with Machine Learning Classification Analysis Approach”, MATRIK: Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer, Vol. 24, No. 2, pp. 235-246, March, 2025 .\u003Cbr>This is an open access article under the CC BY-SA license ([https://creativecommons.org/licenses/by-sa/4.0/](https://creativecommons.org/licenses/by-sa/4.0/)) |  |\n\nJournal homepage: [https://journal.universitasbumigora.ac.id/index.php/matrik](https://journal.universitasbumigora.ac.id/index.php/matrik)  \n1. INTRODUCTION  \nStunting is a condition characterized by impaired linear growth during childhood, representing the most prevalent form of malnutrition worldwide. The physical and neurocognitive impairments associated with this growth disorder can be potentially permanent, posing a major challenge to human development [1] . Malnutrition affects muscle function, weakens the immune system, impairs brain function, and can lead to issues with neurological development [2] . Between 2013 and 2021, Indonesia saw an annual decrease in the stunting rate, averaging between 1.28% and 2.1% each year. In 2021, the Indonesian gov","cbCaiiCWvlxTnlzA","https://ap.wps.com/l/cbCaiiCWvlxTnlzA","pdf",926390,1,12,"English","en",105,"# Introduction\n## Stunting as a prevalent malnutrition condition\n## Prior studies using Naive Bayes and SVM/MLP/CNN\n## Rationale for applying machine learning classification models","[{\"question\":\"What problem does the study address regarding toddlers?\",\"answer\":\"The study focuses on identifying and classifying toddlers’ nutritional status, especially stunting, as a major form of malnutrition affecting development.\"},{\"question\":\"Which machine learning algorithms are used for the classification analysis?\",\"answer\":\"The approach uses Naive Bayes, Support Vector Machine, and Multilayer Perceptron algorithms.\"},{\"question\":\"How is model performance optimized and what dataset is used?\",\"answer\":\"Performance is optimized using gridsearchCV, and the model is trained and evaluated on 6,812 toddler records collected from a Health Center in 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