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All Journal JMM (Jurnal Masyarakat Mandiri) Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya Jurnal Abdimas Mandiri Jurnal Informasi dan Teknologi Abdimas Galuh : Jurnal Pengabdian Kepada Masyarakat Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Mandiri IT Malcom: Indonesian Journal of Machine Learning and Computer Science Journal Of Artificial Intelligence And Software Engineering Electronic Integrated Computer Algorithm Journal AQILA : Acceleration, Quantum, Information Technology and Algorithm Journal
Marcelina, Dona
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Journal : Journal Of Artificial Intelligence And Software Engineering

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Optimization of Stunting Risk Prediction Using a Hybrid Genetic-Machine Learning Model Heryati, Agustina; Terttiaaivini, Terttiaavini; Marcelina, Dona; Romli, Harsi
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6988

Abstract

Stunting is a chronic nutritional problem that remains a national priority issue in Indonesia. According to the 2022 Indonesian Nutrition Status Survey (SSGI), the national stunting prevalence reached 21.6%, with a target reduction to 14% by 2024. Accurate prediction of stunting risk remains a challenge, particularly in regions like Palembang City, which exhibit diverse socio-economic conditions and complex anthropometric characteristics. This study develops a hybrid machine learning model for stunting risk prediction by integrating classification algorithms with a Genetic Algorithm (GA) for feature selection. The hybrid approach aims to enhance predictive accuracy and efficiency based on numerical and socio-economic data. A total of 6,000 samples were used, and after preprocessing (trimming, winsorization, normalization, and SMOTE), 5,366 clean data samples were obtained. Four classification algorithms were tested: Decision Tree, K-Nearest Neighbor, Random Forest, and XGBoost. The best performance was achieved by the XGBoost model, with an accuracy of 84.08%, recall of 93%, and F1-score of 0.91 for the majority class. By integrating the Genetic Algorithm, optimal accuracy reached 95.34% in the third generation of feature selection. This study contributes a hybrid machine learning-based predictive framework that can be adopted by local health institutions for more targeted early detection of stunting risk.
Co-Authors Adi, Dian Abri AHMADI ALFIARINI, ALFIARINI Anandez, Arum Adisha Putra Cahyani, Septa Deni Apriadi dikesuma, hendra Dopri Evi Yulianti Firman Aziz Heryati, Agustina Imelda Saluza, Imelda Indah Permatasari Pratiwi, Indah Putri Putri, Indah Pratiwi Robi Yanto Romli, Harsi Suryati Terttiaaivini, Terttiaavini Terttiaavini, Terttiaavini Terttiavini, Terttiavini Yance Manoppo Yulius, Yosef