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All Journal Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)
Asmi, Nurul Annisa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Classification of Nutritional Status of Pregnant Women at Risk of Stunting in Prospective Babies Using the Support Vector Machine (SVM) Algorithm Afrillia, Yesy; Fadlisyah, Fadlisyah; Asmi, Nurul Annisa
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 1 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i1.22393

Abstract

Stunting describes the existence of chronic nutritional problems, influenced by the condition of mothers/mothers-to-be, fetal period, and infants/toddlers, including diseases suffered during toddlerhood. According to a WHO report quoted from Riskesdas, in 2018 the stunting target in Indonesia was 20%, but in 2013 the stunting rate was 37.2%, but in 2018 there was a decrease to 30.8%. However, the stunting rate in Indonesia is still very high and far from what is targeted by WHO. The method with the best level of accuracy for classification in this study is SVM. This study uses the Support Vector Machine (SVM) method as criteria and attributes which take benchmarks in pregnant women with attributes as a reference including gestational age, maternal weight, blood pressure, and pregnancy problems. The reason for taking benchmarks in pregnant women is because in the first 1000 days of a baby's life determines the baby's nutrition. The first 1000 days of life or 1000 HPK is a critical period in the growth and development of children starting from the beginning of pregnancy (270 days) to 2 years old (730 days). Data was obtained from the Tanah Luas Health Center totaling 684 data on pregnant women. The process of manual calculation is data normalization, kernelization, calculating the alpha and alpha delta Ei values, calculating weights, calculating bias values, and calculating f(x) values. In this study, the dataset totaled 680 data with 544 training data and 136 test data with the criteria of gestational age, pregnant woman's weight, blood pressure, and pregnancy problems. The accuracy obtained was 38.90 %. The variables that have the most influence on this classification are 3, namely the weight of pregnant women, blood pressure, and complaints experienced in pregnant women.
Co-Authors Fadlisyah Fadlisyah Yesy Afrillia