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Predicting Stunting Risk in Toddlers Using Interpretable Machine Learning on Imbalanced Data with Random Forest

Scientific Journal of Informatics · Vol. 13 No. 2 (2026) · hlm. 449–464

DOI
10.15294/sji.v13i2.42131
ISSN
2407-7658, 2460-0040
Terbit
2026-06-22
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

Purpose: The prevalence of stunting in toddlers is still significant at over 20%, according to the Indonesian Nutritional Status Survey (SSGI). By creating machine learning based early prediction models especially made to handle unbalanced datasets, this project seeks to hasten the elimination of stunting. Methods: Three hybrid data balancing methods SMOTE, SMOTE + Tomek Links, and SMOTE + ENN were used in conjunction with a Random Forest algorithm. An anthropometry dataset comprising 40.444 toddlers was used to train and evaluate the models. Data cleaning, labeling, and imbalance handling comprised preprocessing. Accuracy, precision, recall, F1-score, and specificity metrics produced from a confusion matrix were then used for evaluation. Findings: Four key features significantly influence classification: ZSTB/U (0.698961), Height (0.116384), Weight (0.102773), and LiLA (0.035833). The Random Forest algorithm, paired with SMOTE-based techniques, achieved near-perfect performance across all metrics (approaching 1.0). This demonstrates excellent capability in accurately distinguishing the nutritional status of toddlers. Originality: This study offers a scientific explanation of the main stunting variables as well as a high-performance classification methodology. It offers a strong framework for early stunting diagnosis in Indonesia by successfully correcting class imbalance through SMOTE, SMOTE-Tomek, and SMOTE-ENN.

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28 penulis · 107 tautan kepenulisan bersama · 42 lintas organisasi

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  • Islamic University of Indonesia
  • National Research and Innovation Agency
  • STIKES Yayasan RS Dr. Soetomo
  • Poltekkes Kemenkes Yogyakarta
  • Rumah Sakit Dokter Soetomo
  • Poltekkes Kemenkes Palu
  • Siliwangi University
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Rekan penulisOrganisasiArtikel bersama
Utami UtamiIslamic University of Indonesia2
Sri SupadmiNational Research and Innovation Agency2
Vita WidyasariIslamic University of Indonesia1
Emi Azmi ChoironiIslamic University of Indonesia1
Slamet RiyantoNational Research and Innovation Agency1
Agus WijanarkaPoltekkes Kemenkes Yogyakarta1
Ina KusriniNational Research and Innovation Agency1
Taufiq HidayatNational Research and Innovation Agency1
Marizka KhairunnisaNational Research and Innovation Agency1
Sidiq PurwokoNational Research and Innovation Agency1
Tri SuwarnoIslamic University of Indonesia1
Anindya Amanda DamayantiIslamic University of Indonesia1
Fayik Nisaul Balad RosadiIslamic University of Indonesia1
Rana Aulia Farah KamilaIslamic University of Indonesia1
Shafeeq Abiyoga FawrestuIslamic University of Indonesia1
Raafi Haidar ArrasyidIslamic University of Indonesia1
Nisriinaa Saarah Nuur NabiilahIslamic University of Indonesia1
Dwi CahyantiAfiliasi tidak diketahui1
Siti NoviantiSiliwangi University1
Diah Wijayanti SuthaRumah Sakit Dokter Soetomo1
Nathratul Ayeshah ZulkifliAfiliasi tidak diketahui1
ChristinePoltekkes Kemenkes Palu1
Asri Handayani SolihinSekolah Tinggi Ilmu Kesehatan Dharma Husada1
MuhadiPoliteknik Medica Farma Husada Mataram1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/sji/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Predicting Stunting Risk in Toddlers Using Interpretable Machine Learning on Imbalanced Data with Random Forest</dc:title>
	<dc:creator xml:lang="en">Utami</dc:creator>
	<dc:creator xml:lang="en">Ali, Amir</dc:creator>
	<dc:creator xml:lang="en">Faida, Eka Wilda</dc:creator>
	<dc:creator xml:lang="en">Kuntari, Titik</dc:creator>
	<dc:subject xml:lang="en">Prevalence of Stunting</dc:subject>
	<dc:subject xml:lang="en">SMOTE</dc:subject>
	<dc:subject xml:lang="en">SMOTE-Tomek</dc:subject>
	<dc:subject xml:lang="en">SMOTE-ENN</dc:subject>
	<dc:subject xml:lang="en">Prediction models</dc:subject>
	<dc:description xml:lang="en">Purpose: The prevalence of stunting in toddlers is still significant at over 20%, according to the Indonesian Nutritional Status Survey (SSGI). By creating machine learning based early prediction models especially made to handle unbalanced datasets, this project seeks to hasten the elimination of stunting.
Methods: Three hybrid data balancing methods SMOTE, SMOTE + Tomek Links, and SMOTE + ENN were used in conjunction with a Random Forest algorithm. An anthropometry dataset comprising 40.444 toddlers was used to train and evaluate the models. Data cleaning, labeling, and imbalance handling comprised preprocessing. Accuracy, precision, recall, F1-score, and specificity metrics produced from a confusion matrix were then used for evaluation.
Findings: Four key features significantly influence classification: ZSTB/U (0.698961), Height (0.116384), Weight (0.102773), and LiLA (0.035833). The Random Forest algorithm, paired with SMOTE-based techniques, achieved near-perfect performance across all metrics (approaching 1.0). This demonstrates excellent capability in accurately distinguishing the nutritional status of toddlers.
Originality: This study offers a scientific explanation of the main stunting variables as well as a high-performance classification methodology. It offers a strong framework for early stunting diagnosis in Indonesia by successfully correcting class imbalance through SMOTE, SMOTE-Tomek, and SMOTE-ENN.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2026-06-22</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/sji/article/view/42131</dc:identifier>
	<dc:identifier>10.15294/sji.v13i2.42131</dc:identifier>
	<dc:source xml:lang="en">Scientific Journal of Informatics; Vol. 13 No. 2: Regular Issue; 449-464</dc:source>
	<dc:source>2407-7658</dc:source>
	<dc:source>2460-0040</dc:source>
	<dc:source>10.15294/sji.v13i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/sji/article/view/42131/10210</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Scientific Journal of Informatics</dc:rights>
</oai_dc:dc>