BibTex Citation Data :
@article{J.Gauss8099, author = {Ahmad Aditya and Suparti Suparti and Sudarno Sudarno}, title = {KETEPATAN KLASIFIKASI PEMILIHAN METODE KONTRASEPSI DI KOTA SEMARANG MENGGUNAKAN BOOSTSTRAP AGGREGATTING REGRESI LOGISTIK MULTINOMIAL}, journal = {Jurnal Gaussian}, volume = {4}, number = {1}, year = {2015}, keywords = {}, abstract = { Classification is one of the statistical methods in grouping the data compiled systematically. Classification problem rises when there are a number of measures that consists of one or several categories that can not be identified directly but must use a measure. classification methods commonly used in studies to analyze a problem or event is logistic regression analysis. However, this classification method provides unstable parameter estimation. So to obtain a stable parameter multinomial logistic regression model used bootstrap approach that is bootstrap aggregating (bagging). The purpose of this study was to compare the accuracy of the classification multinomial logistic regression models and bootstrap aggragatting model using the data of family planning in Semarang. From the results of bagging multinomial logistic regression obtained classification accuracy in replication bootstrap most 50 times at 51%, this model is able to decrease the classification error of up to 2% compared to the multinomial logistic regression model with a classification accuracy of 49%. Keywords: logistic regression, bootstrap aggregating, accuracy of classification}, issn = {2339-2541}, pages = {11--20} doi = {10.14710/j.gauss.4.1.11-20}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/8099} }
Refworks Citation Data :
Classification is one of the statistical methods in grouping the data compiled systematically. Classification problem rises when there are a number of measures that consists of one or several categories that can not be identified directly but must use a measure. classification methods commonly used in studies to analyze a problem or event is logistic regression analysis. However, this classification method provides unstable parameter estimation. So to obtain a stable parameter multinomial logistic regression model used bootstrap approach that is bootstrap aggregating (bagging). The purpose of this study was to compare the accuracy of the classification multinomial logistic regression models and bootstrap aggragatting model using the data of family planning in Semarang. From the results of bagging multinomial logistic regression obtained classification accuracy in replication bootstrap most 50 times at 51%, this model is able to decrease the classification error of up to 2% compared to the multinomial logistic regression model with a classification accuracy of 49%.
Article Metrics:
Last update:
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Media Statistika journal and Department of Statistics, Universitas Diponegoro as the publisher of the journal. Copyright encompasses the rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations.
Jurnal Gaussian and Department of Statistics, Universitas Diponegoro and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in Jurnal Gaussian journal are the sole and exclusive responsibility of their respective authors and advertisers.
The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form Jurnal Gaussian]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :
Dr. Rukun Santoso (Editor-in-Chief) Editorial Office of Jurnal GaussianDepartment of Statistics, Universitas DiponegoroJl. Prof. Soedarto, Kampus Undip Tembalang, Semarang, Central Java, Indonesia 50275Telp./Fax: +62-24-7474754Email: jurnalgaussian@gmail.com
Jurnal Gaussian by Departemen Statistika Undip is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Visitor Number:
View statistics
