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All Journal Indonesian Journal of Artificial Intelligence and Data Mining
Chen, Kelvin
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Computer Science & IT

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Classification of Big Data Stunting Using Support Vector Regression Method at Stella Maris Medan Maternity Hospital Chen, Kelvin; Adriansyah, R. A. Fattah; Juliandy, Carles; Sinaga, Frans Mikael; Liko, Frederick; Angkasa, Aswin
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.31112

Abstract

This study aims to classify big data related to stunting using the Support Vector Regression (SVR) method at Stella Maris Maternity Hospital, Medan. Stunting, a condition of impaired growth in children due to chronic malnutrition and repeated infections, affects physical and cognitive development. With increasing health data, big data processing methods are essential for accurate information. SVR was chosen for handling high-dimensional and non-linear data, providing precise results. The study uses medical information, nutritional history, and socio-economic factors collected from hospital patients. The research process includes data collection, pre-processing to address missing values and outliers, normalization, and SVR application. Final results use SVR with Voting Classifier combining Support Vector Classifier (SVC), Random Forest (RF), and Gradient Boosting (GB), achieving an accuracy of 91.67%. This approach effectively identifies main stunting factors, aiding clinical decision-making and intervention programs. The study showcases big data and machine learning's potential in healthcare, serving as a model for improving health services and monitoring children's health conditions.
Co-Authors Adriansyah, R. A. Fattah Angkasa, Aswin Carles Juliandy Liko, Frederick Sinaga, Frans Mikael