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Endro Setyo Cahyono
Department of Mathematics, Faculty of Mathematics and Natural Science, Sriwijaya University, 30662, Indralaya, South Sumatra, Indonesia.
Computer Science & IT Environmental Science

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Prediction and Classification of Low Birth Weight Data Using Machine Learning Techniques Alfensi Faruk; Endro Setyo Cahyono
Indonesian Journal of Science and Technology Vol 3, No 1 (2018): IJoST: VOLUME 3, ISSUE 1, April 2018
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/ijost.v3i1.10799

Abstract

Machine learning (ML) is a subject that focuses on the data analysis using various statistical tools and learning processes in order to gain more knowledge from the data. The objective of this research was to apply one of the ML techniques on the low birth weight (LBW) data in Indonesia. This research conducts two ML tasks; including prediction and classification. The binary logistic regression model was firstly employed on the train and the test data. Then; the random approach was also applied to the data set. The results showed that the binary logistic regression had a good performance for prediction; but it was a poor approach for classification. On the other hand; random forest approach has a very good performance for both prediction and classification of the LBW data set
Co-Authors Alfensi Faruk