[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121548-en":3,"doc-seo-121548-105":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":4,"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},121548,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Explainable Machine Learning Models SHAP-based for Feature Importance Affecting Stunting Prevalence","Stunting is a chronic nutritional deficiency in toddlers and a major public health issue due to its long-term effects on growth and development. In Indonesia, especially Sumatra Province, this study assesses logistic regression and three machine learning approaches—decision tree, random forest, and SVM—for classifying stunting prevalence relative to the 2024 national threshold. Because predictive models can be accurate yet difficult to interpret, the study applies SHAP to the best model to identify key contributors. Results show random forest attains 90.00% accuracy, and SHAP highlights underweight as the most influential predictor, supporting interpretable policy decisions for stunting reduction.","ComTech: Computer, Mathematics and Engineering Applications, 17(1), June 2026, 11−22  \nDOI: 10. 21512/comtech.v17i1 .13732  \nP-ISSN: 2087-1244  \nE-ISSN: 2476-907X  \nExplainable Machine Learning Models SHAP-based for Feature Importance Affecting Stunting Prevalence  \nAsysta Amalia Pasaribu1*; Nur Fitriyani Sahamony2 ;  \nKhairil Anwar Notodiputro3 ; Bagus Sartono4  \n1,2,3,4Study Program of Statistics and Data Science, IPB University, Bogor, Indonesia 16680 1Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480 2Digital Business Study Program, Faculty of Business and Social Science, Binawan University,  \nJakarta, Indonesia 13630  \n[1](1asysta.amalia@binus.ac.id)[asysta.amalia@binus.ac.id](1asysta.amalia@binus.ac.id); [2](2kawaiisahamony@apps.ipb.ac.id)[kawaiisahamony@apps.ipb.ac.id](2kawaiisahamony@apps.ipb.ac.id);  \n[3](3khairil@apps.ipb.ac.id)[khairil@apps.ipb.ac.id](3khairil@apps.ipb.ac.id); [4](4bagusco@apps.ipb.ac.id)[bagusco@apps.ipb.ac.id](4bagusco@apps.ipb.ac.id)  \nReceived: 10th June 2025/ Revised: 27th October 2025/ Accepted: 24th November 2025  \nHow to Cite: Pasaribu, A. A., Sahamony, N. F., Notodiputro, K. A., & Sartono, B. (2025). Explainable machine learning models SHAP-based for feature importance affecting stunting prevalence. ComTech: Computer, Mathematics and Engineering Applications, 17(1), 11−22. [https://doi.org/10.21512/comtech.v17i1.13732](https://doi.org/10.21512/comtech.v17i1.13732)  \nAbstract - Stunting is a form of chronic nutritional deficiency in toddlers and remains a major public health concern due to its impact on child growth and development. Efforts to reduce its prevalence continue to be strengthened in Indonesia, particularly in Sumatra Province. This study aims to evaluate the accuracy of a logistic regression model and three machine learning models—decision tree, random forest, and support vector machine (SVM)—in classifying stunting prevalence. The response variable is defined as the prevalence of stunting among toddlers, categorized into two classes: exceeding the national target and not exceeding the national target, based on the 2024 national threshold. Although classification models can provide accurate predictions, they often lack interpretability. Therefore, this study applies the SHAP method to the best-performing machine learning model to identify the key factors influencing stunting. The use of Shapley values is justified through the uniqueness theorem, which establishes it as the only attribution method satisfying desirable fairness properties. SHAP values are employed to explain the model by referencing both the trained model and the underlying data. The results show that the random forest model achieves the highest accuracy (90.00%), outperforming the other models. SHAP analysis reveals that Underweight is the most influential predictor contributing to stunting prevalence in Sumatra Province. These findings highlight the relevance of machine learning interpretability in supporting policy decisions for stunting reduction.  \nKeywords: feature importance; logistic regression; explanaible machine learning; SHAPE value; stunting prevalence  \nI. INTRODUCTION  \nStunting is a condition of growth failure in children caused by chronic malnutrition, resulting ina child's height being shorter than that of their peers. Stunting is a serious problem currently facing the world (Rifada et al., 2023) . particularly in poor and developing countries such as Indonesia (Ashari et al., 2023) . Stunting is one of the challenges facing the Indonesian government in developing a national strategy for stunting prevention. Stunting is a condition of reduced height in children caused by malnutrition and persisting over a long period. This results in achild's height being shorter than that expected for their age. Efforts to reduce prevalence in Indonesia are being made to align with global targets, namely the National target, namely efforts to accelerate the re","cbCainnglry2xIwN","https://ap.wps.com/l/cbCainnglry2xIwN","pdf",1298119,1,12,"English","en",105,"# I. 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