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All Journal Elkawnie Riau Journal of Computer Science Sinkron : Jurnal dan Penelitian Teknik Informatika Rang Teknik Journal JURNAL SIMTIKA (Sistem Informasi dan Informatika) MONSU'ANI TANO Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Emerging Information Science and Technology Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Informatika Teknologi dan Sains (Jinteks) J-Icon : Jurnal Komputer dan Informatika SATIN - Sains dan Teknologi Informasi Bulletin of Informatics and Data Science Jurnal Pustaka Paket
Lilik Suhery, Lilik
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Journal : Bulletin of Informatics and Data Science

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Hybrid Gradient Boosting and SMOTE-ENN for Toddler Nutritional Status Classification on Imbalanced Data Sinlae, Alfry Aristo Jansen; Erkamim, Moh.; Fitriyadi, Farid; Suhery, Lilik; Destriana, Rachmat
Bulletin of Informatics and Data Science Vol 3, No 2 (2024): November 2024
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v3i2.93

Abstract

Stunting in toddlers remains a serious global health issue with long-term impacts on children's physical and cognitive development. One of the main challenges in classifying nutritional status is class imbalance, where the number of normal cases significantly exceeds that of minority classes such as stunted and severely stunted. This study aims to develop a hybrid approach by integrating the Gradient Boosting algorithm with the SMOTE-ENN (Synthetic Minority Oversampling Technique–Edited Nearest Neighbors) method to improve classification performance on imbalanced data. The dataset used was obtained from the Kaggle platform, consisting of 121,000 entries with four nutritional status categories. Data preprocessing included label encoding, numerical feature standardization, and stratified data splitting with an 80:20 ratio. The model was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The proposed hybrid model successfully increased the recall for the stunted class from 61.80% to 98.41%, and the F1-score from 71.93% to 83.58%. Overall accuracy improved from 92.39% to 93.35%, while the ROC-AUC score increased from 99.08% to 99.63%. These findings demonstrate that integrating Gradient Boosting with SMOTE-ENN is effective in enhancing sensitivity to minority classes and improving overall multi-class classification performance.
Co-Authors Alamsyah, Dedy Alfry Aristo Jansen Sinlae Ananda, Suci Rizki Arif Budiman Arif Rizki Marsa Deny Ariestiandy Destriana, Rachmat Dilson Dilson -, Dilson Dio Marta Dinata Fatma Ira Wahyuni Fitriyadi, Farid Fryda Fatmayati Hanifah Asnur Hariyadi, Hariyadi Juli Budi Satya Jusmawati Marlisa, Yessa Moh. Erkamim MS Firman Maulana Noviardi Noviardi Noviardi, Noviardi Rafi Anggara Ramacos Fardela Ridha Sari Rini Yunita Rini Yunita Ronny Junnaidi Siska Arimadona Sri Tria Siska Sutria Desman Syamsuir, Elvi Uliyatunisa Uliyatunisa Umar Khatab Umar Khatab Veithzal Rivai Zainal Yanuardi