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Wiwien Hadikurniawati
Universitas Stikubank
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Journal : Journal of Applied Data Sciences

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A Dual-Fusion Hybrid Model with Attention for Stunting Prediction among Children under Five Years Hadikurniawati, Wiwien; Hartomo, Kristoko Dwi; Sembiring, Irwan; Arthur, Christian
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.831

Abstract

Malnutrition remains a persistent global health challenge, especially among children under five. Traditional assessment methods often rely on static anthropometric measures, which are limited in capturing complex growth patterns. This study aims to develop a robust classification model for predicting the nutritional status of children under five years old, addressing the critical public health challenge of stunting. The model contributes to the growing need for accurate, data-driven early detection systems in child health monitoring by introducing a hybrid framework that combines deep learning and classical machine learning techniques. The proposed approach integrates automatically extracted features from a One-Dimensional Convolutional Neural Network (1D-CNN) with classical anthropometric indicators. These combined features are processed through an additive attention mechanism, highlighting the most informative attributes. The attention-weighted representation is then classified using an ensemble stacking method that aggregates predictions from multiple base classifiers, including decision trees, nearest neighbor algorithms, support vector machines, etc. Synthetic Minority Over-sampling Technique (SMOTE) is applied to the training dataset to mitigate data imbalance, particularly the underrepresentation of severe and moderate malnutrition cases. The research utilizes a dataset comprising 2,789 records of children under five years old collected from community health posts in Indonesia. Data preprocessing included cleaning, normalization, and gender encoding. The model’s performance was evaluated using 5-fold cross-validation and measured by accuracy, precision, recall, and area under the curve metrics. The results show that the proposed model achieved an average accuracy of 99.70% and an area under the curve of 99.99%. An ablation study further demonstrated the significant contribution of each component, feature extraction, fusion mechanism, and ensemble classifier to the final performance. This approach reveals a robust and scalable solution for early nutritional status prediction in healthcare settings.
Co-Authors Abriyanto, Oky Achmad Solichan Ade Iriani Adhi Mulya, Ananda Agung Prihandono Aji Supriyanto Almin, Imam Husni Al Alviani Setya Yuniantika Andiany, Fairuz Eka Andiany, Fairuz Eka Angga Dewandono Anindita Septiarini, Anindita Anindita Septirini Anwar, Muchamad Taufiq Arief Jananto Arthur, Christian Brian Datuesa, Egga Cahyono, Taufik Dwi Danny Manongga Eddy Nurraharjo Edy Winarno Eka Andiany, Fairuz Eka Ardhianto Eko Nur Wahyudi Endang Lestariningsih EngMarkiano Solissa, Everhard Erika Dewi Anggraini Fadhilah, Lailatul Farih, Nia Nailil Fatkhul Amin fatkhurrizqi, Maolana Galih Setiadi Gunawan, Doni Triyoga Gusti Ari Prasetyo Hamdani Hamdani Hamdani Hamdani Heribertus Yulianton Herny Februariyanti Heru Yudia Setyo Anggoro Hindriyanto Dwi Purnomo Huda, Azis Fatchul Imam Husni Al Amin Indriani, Vanyariska Irfak Lahumu Darojat Irwan Sembiring Ivannofick Adha Nugraha Khabib Mustofa Kristoko Dwi Hartomo M Arip Islahudin Mohammad Faisal Fahrul Monica Yoshe Titimeidara Muchamad Maskhur Muji Sukur Putra Ramdani, Aditya Rudi Hariyanto Sari, Lenny Estika Sarita Yuniarti Hanum Setyawan Wibisono Setyawan Wibisono Setyawan Wibisono Sidiq Eko Budiharjo Sri Eniyati Suci ananda sari Sutarto Wijono Taufiq Dwi Cahyono Tegar Prabowo, Satria Veronica Lusiana Wijayanti, Tri Cicik Zakaria, Franky Zuly Budiarso