BibTex Citation Data :
@article{J.Gauss10219, author = {Fajar Setiawan and Rita Rahmawati and Suparti Suparti}, title = {KETEPATAN KLASIFIKASI KEIKUTSERTAAN KELUARGA BERENCANA MENGGUNAKAN REGRESI LOGISTIK BINER DAN REGRESI PROBIT BINER (Studi Kasus di Kabupaten Semarang Tahun 2014)}, journal = {Jurnal Gaussian}, volume = {4}, number = {4}, year = {2015}, keywords = {Keluarga Berencana (KB), Binary Logistic Regression, Binary Probit Regression, Classification, Confusion}, abstract = { Population growth in Indonesia has increasedeach year. According to the population sensus conducted by National Statistics Bureau in 2010, Indonesia's population reached 237,6 million. Therefore, to control the population growth rate, government hold Keluarga Berencana (KB) or family planning program for couples in the childbearing age. The aim of this thesis which analyze the classification of couples in the childbearing age who follow family planning program, is to reduce the birth rate. So that, population can be controlled. The data used in this study is a Semarang Regency updated family data in 2014 that conducted Nasional Population and Family Panning Bureau. From the data, a binary logistic regression model and binary probit regression will be obtained, also classification accuracy will be obtained from each of these models. The analysis showed that the Binary Logistic Regression method produces a classification accuracy of 69,0% with 31,0% classification error. While, Probit Binary Regression method produces a classification accuracy of 68,4% with 31,6% misclassification. Binary Logistic Regression and Binary Logistic Regression method have a differences classification accuracy was very small then both are relative similar for analyze the classification family planning in Semarang Regency. Keywords: Keluarga Berencana (KB), Binary Logistic Regression, Binary Probit Regression, Classification, Confusion }, issn = {2339-2541}, pages = {845--854} doi = {10.14710/j.gauss.4.4.845-854}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/10219} }
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Population growth in Indonesia has increasedeach year. According to the population sensus conducted by National Statistics Bureau in 2010, Indonesia's population reached 237,6 million. Therefore, to control the population growth rate, government hold Keluarga Berencana (KB) or family planning program for couples in the childbearing age. The aim of this thesis which analyze the classification of couples in the childbearing age who follow family planning program, is to reduce the birth rate. So that, population can be controlled. The data used in this study is a Semarang Regency updated family data in 2014 that conducted Nasional Population and Family Panning Bureau. From the data, a binary logistic regression model and binary probit regression will be obtained, also classification accuracy will be obtained from each of these models. The analysis showed that the Binary Logistic Regression method produces a classification accuracy of 69,0% with 31,0% classification error. While, Probit Binary Regression method produces a classification accuracy of 68,4% with 31,6% misclassification. Binary Logistic Regression and Binary Logistic Regression method have a differences classification accuracy was very small then both are relative similar for analyze the classification family planning in Semarang Regency.
Keywords: Keluarga Berencana (KB), Binary Logistic Regression, Binary Probit Regression, Classification,
Confusion
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