BibTex Citation Data :
@article{J.Gauss39188, author = {Riza Sahila and Tatik Widiharih and Iut Utami}, title = {Analisis Klasifikasi Menggunakan Regresi Logistik Biner Dan Algoritma Naive Bayes Classifier Pada Penyakit Hipertensi}, journal = {Jurnal Gaussian}, volume = {13}, number = {2}, year = {2024}, keywords = {Keywords: Hypertension; Classification; Binary Logistic Regression; Naïve Bayes Classifier; Sensitivity.}, abstract = { Hypertension is a primary cause of cardiovascular disease. Approximately 60% of people with hypertension are in developing countries, including Indonesia. In this analytical study, classification will be carried out to prove the status of hypertensive patients or not hypertensive. The classification method used is Binary Logistic Regression and Naïve Bayes Classifier. Binary Logistic Regression is Logistic Regression with the response variable being binary. Naïve Bayes Classifier namely predicting future opportunities using previous data. The factors used in this study were gender, age, height, and weight. The greatest accuracy of classification results is in the proportion of 90%:10%. The accuracy of the classification produced by Binary Logistic Regression method resulted in a sensitivity of 93.33%. The classification accuracy obtained by the Naïve Bayes Classifier with a sensitivity of 63.64%. This shows that the Binary Logistic Regression method has a better sensitivity value. Keywords: Hypertension, Classification, Binary Logistic Regression, Naïve Bayes Classifier, Sensitivity. }, issn = {2339-2541}, pages = {319--327} doi = {10.14710/j.gauss.13.2.319-327}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/39188} }
Refworks Citation Data :
Hypertension is a primary cause of cardiovascular disease. Approximately 60% of people with hypertension are in developing countries, including Indonesia. In this analytical study, classification will be carried out to prove the status of hypertensive patients or not hypertensive. The classification method used is Binary Logistic Regression and Naïve Bayes Classifier. Binary Logistic Regression is Logistic Regression with the response variable being binary. Naïve Bayes Classifier namely predicting future opportunities using previous data. The factors used in this study were gender, age, height, and weight. The greatest accuracy of classification results is in the proportion of 90%:10%. The accuracy of the classification produced by Binary Logistic Regression method resulted in a sensitivity of 93.33%. The classification accuracy obtained by the Naïve Bayes Classifier with a sensitivity of 63.64%. This shows that the Binary Logistic Regression method has a better sensitivity value.
Keywords: Hypertension, Classification, Binary Logistic Regression, Naïve Bayes Classifier, Sensitivity.
Note: This article has supplementary file(s).
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
