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All Journal Jurnal Informatika Progres
Emil Aguslaim Habi Thalib
Informatika, Universitas Muhammadiyah Makassar
Computer Science & IT

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KLASIFIKASI MULTI-CLASS STATUS GIZI BALITA MENGGUNAKAN ARSITEKTUR DEEP NEURAL NETWORK Alizha Nur Arspandy; Desi Anggreani; Muhyiddin A.M Hayat; Muhammad Faisal; Muhammad Syafaat; Indriyanti; Emil Aguslaim Habi Thalib
PROGRESS Vol 18 No 2 (2026): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i2.464

Abstract

This study aims to develop a classification model for toddler nutritional status using a Deep Neural Network (DNN) with a multi-class classification approach. The research utilizes anthropometric data of toddlers aged 0-60 months obtained from UPTD Puskesmas Cendana Putih, North Luwu Regency, covering the period 2023–2025. The dataset consists of 156 records with features including age, weight, height, and Z-score indicators. Data preprocessing involves validation, normalization, and splitting into training and testing sets with a ratio of 85:15. The DNN model is constructed with multiple hidden layers (128, 64, and 32 neurons) and trained using the Adam optimizer and categorical cross-entropy loss function. The results show that the model achieves an accuracy of 91.67% on the testing data, indicating good performance in classifying nutritional status into categories such as undernutrition, normal, and obesity. Evaluation using confusion matrix and classification metrics (precision, recall, and F1-score) reveals that the model performs well on dominant classes but shows limitations in minority classes due to data imbalance. Overall, the proposed model demonstrates potential as a decision support tool to assist healthcare workers in identifying toddler nutritional status more accurately and efficiently.
Co-Authors Alizha Nur Arspandy Desi Anggreani Indriyanti Muhammad Faisal Muhammad Syafaat S. Kuba Muhyiddin A.M Hayat