Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Muhammad Rizkan Arif
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Klasifikasi Berat Badan Lahir Rendah Pada Bayi Dengan Fuzzy K-Nearest Neighbor Muhammad Rizkan Arif; Budi Darma Setiawan; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (914.652 KB)

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%.
Co-Authors Budi Darma Setiawan Marji Marji