Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Vol 3 No 9 (2019): September 2019

Klasifikasi Berat Badan Lahir Rendah Pada Bayi Dengan Fuzzy K-Nearest Neighbor

Muhammad Rizkan Arif (Fakultas Ilmu Komputer, Universitas Brawijaya)
Budi Darma Setiawan (Fakultas Ilmu Komputer, Universitas Brawijaya)
Marji Marji (Fakultas Ilmu Komputer, Universitas Brawijaya)



Article Info

Publish Date
20 Aug 2019

Abstract

The number of infant mortality (IMR) is a measure of the success of health services in an area. The lower the IMR, the better the health services in the area. However, in 2015, the IMR value in Indonesia was very far from the agreed target as an indicator of the success of health service development. In 2013, there was an increase in LBW cases during the 2009-2013 period to 16% according to data from WHO and UNICEF. If viewed from the cause of death, low birth weight babies still rank high. As many as 2.79% of infants died from LBW in East Java in 2010. This percentage increased to 3.32% in 2013 so that LBW was classified as the main cause of neonatal death, which was 38.03% of the total birth rate. The existence of an early detection system is likely that LBW is expected to be able to help reduce infant mortality. One method that can be applied to predict the possibility of LBW is Fuzzy K-Nearest Neighbor (FK-NN). This method is proven to be able to carry out LBW classification with an accuracy rate of 79%.

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Journal Info

Abbrev

j-ptiik

Publisher

Subject

Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Description

Jurnal Pengembangan Teknlogi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya merupakan jurnal keilmuan dibidang komputer yang memuat tulisan ilmiah hasil dari penelitian mahasiswa-mahasiswa Fakultas Ilmu Komputer Universitas Brawijaya. Jurnal ini diharapkan dapat mengembangkan penelitian ...