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Suryani Agustin
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Klasifikasi Berat Badan Lahir Rendah (BBLR) Pada Bayi Dengan Metode Learning Vector Quantization (LVQ) Suryani Agustin; Budi Darma Setiawan; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Low Birth Weight (LBW) is the condition as a birth weight of a baby less than 2500 grams or 2.5 kg.. LBW is a factor of infant mortality in Indonesia. The prevention and treatment of pregnant women when they know they will give birth to babies with LBW are very necessary, in order to minimize the death during the birth process. Therefore, it is expected that the existence of a low birth weight classification system in infant can help to identify the condition of the baby in pregnant women before the baby is born. This research use the Learning Vector Quantization (LVQ) method with 96 data and 6 features, there are age, education, parity, birth interval, hemoglobin and nutritional status. Those who will classify into two classes first is case class, which means the baby is born with LBW and the control class means that the baby is born without LBW. Based on the results of testing, the system produces an average accuracy is 60.5% using optimal parameters for learning rate 0.1, learning rate decrement 0.1 and maximum epoch is 5. In the k-fold cross validation testing the best accuracy value is 58.3% and the average accuracy is 46.85%.
Co-Authors Budi Darma Setiawan M. Ali Fauzi