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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal of Artificial Intelligence and Data Mining Journal Information System Development Bisma: Jurnal Bisnis dan Manajemen Jiko (Jurnal Informatika dan komputer) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Abdimas ADPI Sains dan Teknologi PHARMACOLOGY, MEDICAL REPORTS, ORTHOPEDIC, AND ILLNESS DETAILS (COMORBID) At-Tuhfah : Jurnal Studi Keislaman
Ferawaty, Ferawaty
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Addressing Class Imbalance in Stunting Classification Using SMOTE Enhanced Random Forest Belferik, Ronald; Sinaga, Frans Mikael; Ferawaty, Ferawaty; Manullang, Mangasa A.S.; Sinaga, Tetti
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15349

Abstract

Stunting is a chronic nutritional problem that poses serious long-term effects on children’s health, including impaired physical growth, delayed cognitive development, and reduced productivity in adulthood. Early and accurate detection of stunting is therefore essential to support effective public health interventions and targeted policy implementation. However, one of the central challenges in developing machine learning models for this purpose is the presence of class imbalance in health-related datasets. Such imbalance frequently leads to biased classifiers that perform well on majority classes but fail to identify minority categories, reducing the overall reliability of the system. To overcome this issue, the present study utilized the Synthetic Minority Oversampling Technique (SMOTE) to balance the distribution of classes in a dataset containing 110,000 records. A Random Forest algorithm was then employed as the base classifier, with hyperparameter optimization carried out using the Optuna framework to ensure robustness and generalizability. The experimental results demonstrate that the combined application of SMOTE and Optuna significantly improved classification performance, producing the highest Macro Area Under the Curve (AUC) of 0.9972. This outstanding score indicates the model’s superior ability to distinguish nutritional status categories across both majority and minority classes. The study concludes that addressing data imbalance through oversampling is a fundamental methodological step in constructing fair and effective machine learning systems for stunting detection, ultimately contributing to improved health outcomes and evidence-based policy design.
A Smart Architecture for Stunting Prediction: Implementing the SOM–Voting Classifier on Healthcare Big Data Kelvin, Kelvin; Winardi, Sunaryo; Sinaga, Frans Mikael; Hardy, Hardy; Panjaitan, Erwin Setiawan; Wong, Ng Poi; Ferawaty, Ferawaty; Lim, Justine; Wijaya, Grace Putri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38000

Abstract

Childhood stunting is a persistent public health challenge in Indonesia. This study developed a predictive classification model using healthcare data from hospitals in Medan to enable early identification of at-risk children. A novel framework was proposed that integrated an unsupervised Self-Organizing Map (SOM) for feature engineering with a supervised Voting Classifier ensemble, which combined a Support Vector Classifier (SVC), Random Forest (RF), and Gradient Boosting (GB). The proposed framework achieved an accuracy of 100% on the test set, a substantial improvement over the 91.67% accuracy of the baseline Voting Classifier without SOM. While this result highlighted the model's high predictive potential, it must be interpreted cautiously, acknowledging the need for validation on more diverse datasets to ensure generalizability. The findings demonstrated that this hybrid machine learning approach can serve as a powerful decision-support tool, enabling proactive clinical interventions and aiding public health officials in strategically allocating nutritional resources to support Indonesia's national stunting reduction goals.
Co-Authors Anggraeni, Adilla Antonio, Wakky Arimbi, Siti Nurhaliza Belferik, Ronald Chandra, Wenripin Christin, Theresia Daniel Cassa Augustinus, Daniel Cassa Elisabeth Lintong Hardy Hardy Hilda Hilda Idris, Nur Alfia Ivanka, Kelvin Jeslyn, Jeslyn Kelvin Kelvin Lee, Melvin Legiran Legiran Lim, Justine Lintong, Elisabeth Marlina Sari Manulang, Mangasa A. S. Manullang, Mangasa A.S. Muis, Andi. Abd Nabila, Amanda Salsa Ng Poi Wong Pangaribuan, Jefri Junifer Panjaitan, Erwin Setiawan Ramadhani, Fira rasyid, hilda Romindo RR. Ella Evrita Hestiandari Sinaga, Frans Mikael Sinaga, Tetti Tiang, Steven Wijaya, Grace Putri Winardi, Sunaryo Zulkhair Ali