NDLI: Classification accuracy on the family planning participation status using kernel discriminant analysis

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Classification accuracy on the family planning participation status using kernel discriminant analysis

Content Provider	Scilit
Author	Suparti Sugito Kurniawan, Dian
Copyright Year	2018
Description	Journal: Journal of Physics: Conference Series Population growth in Indonesia has increased every year. According to the population census conducted by the Central Bureau of Statistics (BPS) in 2010, the population of Indonesia has reached 237.6 million people. Therefore, to control the population growth rate, the government hold Family Planning or Keluarga Berencana (KB) program for couples of childbearing age. The purpose of this program is to improve the health of mothers and children in order to manifest prosperous society by controlling births while ensuring control of population growth. The data used in this study is the updated family data of Semarang city in 2016 that conducted by National Family Planning Coordinating Board (BKKBN). From these data, classifiers with kernel discriminant analysis will be obtained, and also classification accuracy will be obtained from that method. The result of the analysis showed that normal kernel discriminant analysis gives 71.05 % classification accuracy with 28.95 % classification error. Whereas triweight kernel discriminant analysis gives 73.68 % classification accuracy with 26.32 % classification error. Using triweight kernel discriminant for data preprocessing of family planning participation of childbearing age couples in Semarang City of 2016 can be stated better than with normal kernel discriminant.
Related Links	http://iopscience.iop.org/article/10.1088/1742-6596/1025/1/012111/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/1025/1/012111
Journal	Journal of Physics: Conference Series
Issue Number	1
Volume Number	1025
Language	English
Publisher	IOP Publishing
Publisher Date	2018-05-30
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Hardware and Architecture Kernel Discriminant Analysis
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy

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