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Emanuell Deftavalandra Rahmanto
Universitas Kristen Satya Wacana
Computer Science & IT Electrical & Electronics Engineering

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Optimization of Support Vector Machine for the Classification of Nutritional Status in Children Emanuell Deftavalandra Rahmanto; Charitas Fibriani (SCOPUS ID=57192643331)
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6192

Abstract

Toddlerhood is a critical developmental period that requires precise nutritional monitoring. However, automated classification systems are often challenged by imbalanced data, which makes minority classes difficult to detect accurately. This study aims to optimize a Support Vector Machine (SVM) using a polynomial kernel to improve detection sensitivity for critical classes. By excluding BMI features to avoid redundancy, the proposed model achieved an accuracy of 98%. The main novelty of this research lies in its achievement of an F1 Macro Score of 0.86, confirming that the model provides balanced and reliable classification performance across all nutritional status categories. These results demonstrate the model’s superiority in identifying the minority classes of Severe Malnutrition and Undernutrition more effectively than previous studies. Therefore, the model is highly recommended as an objective decision support system for the early detection of stunting.
Co-Authors Charitas Fibriani (SCOPUS ID=57192643331)