PERBANDINGAN METODE KLASIFIKASI REGRESI LOGISTIK BINER DAN NAIVE BAYES PADA STATUS PENGGUNA KB DI KOTA TEGAL TAHUN 2014

Published: 30 Apr 2015.
Open Access

Citation Format:
Abstract

Indonesia is a country that includes having the highest population density in the world.It is because the Indonesian state has a birth rate is so high. One of the efforts to control  that population growth can be controlled by using the Keluraga Berencana program. In this study, the method used is the Binary Logistic Regression and Naive Bayes. To perform classification KB User Status in Tegal 2014, the variable used is the wife’s age, the age of first marriage, type of wife’s job, type of husband’s job, wife's education, husband's education, and number of children. The training data comparison testing is 70:30. Based on the research results using binary logistic regression showed that a significant predictor variables that affect the status of keluarga Berencana user  are wife’s age, type of wife’s job, and number of children with a classification accuracy of testing data 83.33% .While with  the Naive Bayes method obtained classification accuracy of 81.75%. From this analysis it can be concluded that the Binary Logistic Regression method is better than the Naive Bayes in classifying the status of KB users in Tegal 2014.

 

Keywords :  Binary Logistic Regression, Naive Bayes, Keluarga Berencana, Classification.

Keywords: Binary Logistic Regression, Naive Bayes, Keluarga Berencana, Classification.

Article Metrics: