Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika
Zul Indra
Department of Information System Universitas Riau, Indonesia
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
The Comparison of Accuracy on Classification Climate Change Data with Logistic Regression Arisman Adnan; Anne Mudya Yolanda; Gustriza Erda; Noor Ell Goldameir; Zul Indra
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2023): Articles Research Volume 8 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11914

Abstract

Machine learning methods can be used to generate climate change models. The goal of this study is to use logistic regression machine learning algorithms to classify data on greenhouse gas emissions. The data used is climate change data of several countries obtained from The World Bank, with total greenhouse gas emissions as the response variable and 61 other attributes as explanatory variables. This data is preprocessed using min-max normalization to handle unbalanced ranges, and then the data is split into 70% training data and 30% testing data. Based on the logistic regression modeling, it was discovered that the data from the min-max transformation resulted in better modeling than the data modeling without the transformation process. The accuracy, precision, sensitivity, and specificity of the transformation are 87.60%, 87.76%, 87.04%, and 88.14%, respectively
Co-Authors Anne Mudya Yolanda Arisman Adnan Goldameir, Noor Ell Gustriza Erda