BibTex Citation Data :
@article{J.Gauss8420, author = {Agung Waluyo and Moch. Mukid and Triastuti Wuryandari}, title = {PERBANDINGAN ANALISIS KLASIFIKASI NASABAH KREDIT MENGGUNAKAN REGRESI LOGISTIK BINER DAN CART (CLASSIFICATION AND REGRESSION TREES)}, journal = {Jurnal Gaussian}, volume = {4}, number = {2}, year = {2015}, keywords = {credit status, logistic regression, CART}, abstract = { Credit is the greatest asset managed by the bank and also the most dominant contributor to the bank's revenue. Debtor to pay credit to the bank may smoothly or jammed. There is a relationship variables that affect a debtor smoothly or jammed in paying credit. This study aims to identify the variables that affect a debtor's credit status. The variables used in this study were gender, education level, occupation, marital status and income. Analytical methods used include Binary Logistic Regression analysis and CART (classification and regression trees). Classification accuracy of the two methods will be compared. Based on the research results of binary logistic regression showed that the variables that affect the debtor's credit status is revenue with 80% classification accuracy. While the results of CART (classification and regression trees) in the form of a decision tree shows the type of work chosen as the root node spliting, with a classification accuracy of 81%. Keywords : credit status, logistic regression, CART }, issn = {2339-2541}, pages = {215--225} doi = {10.14710/j.gauss.4.2.215 - 225}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/8420} }
Refworks Citation Data :
Credit is the greatest asset managed by the bank and also the most dominant contributor to the bank's revenue. Debtor to pay credit to the bank may smoothly or jammed. There is a relationship variables that affect a debtor smoothly or jammed in paying credit. This study aims to identify the variables that affect a debtor's credit status. The variables used in this study were gender, education level, occupation, marital status and income. Analytical methods used include Binary Logistic Regression analysis and CART (classification and regression trees). Classification accuracy of the two methods will be compared. Based on the research results of binary logistic regression showed that the variables that affect the debtor's credit status is revenue with 80% classification accuracy. While the results of CART (classification and regression trees) in the form of a decision tree shows the type of work chosen as the root node spliting, with a classification accuracy of 81%.
Keywords: credit status, logistic regression, CART
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