Support vector machines for classification of low birth weight in Indonesia
Ning Eliyati, Alfensi Faruk, Endang Sri Kresnawati, Ika Arifieni · Journal of Physics Conference Series · 2019
Abstract This paper proposes support vector machines (SVMs), which is currently one of the most popular algorithms in machine learning (ML), in order to classify the low birth weight (LBW) data. The main objectives of this study are to predict the classification of LBW data in Indonesia based on the SVMs andto compare the performance of the proposed SVMs with the binary logistic regression as the most common model for classification of LBW data. The obtained samples were based on the results of Indonesian Demographic and Health Survey in 2012. The results showed that SVMs with four kernel functions (linear, radial, polynomial and hyperbolic tangent) were fit well to the LBW data in Indonesia. Furthermore, the constructed SVMs based on linear kernel function had the best performance among the SVMs with the other proposed kernel functions. This research also concluded that the SVMs based on linear kernel competed well with thebinary logistic regression forclassification LBW data in Indonesia.