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All Journal Indonesian Journal of Artificial Intelligence and Data Mining SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Paradoks : Jurnal Ilmu Ekonomi Advances in Management & Financial Reporting
Hardy Hardy
STMIK Mikroskil
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

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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 Asrul Asrul Ferawaty, Ferawaty Irpan Pardosi Kelvin Kelvin Lim, Justine Marsela, Ibrahim Maslim Maslim, Maslim Megawaty, Megawaty Ng Poi Wong Panjaitan, Erwin Setiawan Pipin, Sio Jurnalis R.A, Ria Andriany Salam, Karta Negara Sinaga, Frans Mikael Tanti Tanti Wijaya, Grace Putri William William Winardi, Sunaryo