The Comparison of Accuracy on Classification Climate Change Data with Logistic Regression

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

World Greenhouse Gas Emission Classification Using Support Vector Machine (Svm) Method

“…Research using the support vector machine method in the environmental field, especially regarding climate change, is rarely conducted. Climate change-related research has been carried out by Adnan et al, (2023) using logistic regression methods with the results 87.60% accuracy, 87.76% precision, 87.04% recall, and 88.14% spesificity. This study will classify greenhouse gas emissions using the support vector machine (SVM) method by applying four different kernel functions and evaluate which kernel function performs best in classifying greenhouse gas emissions.…”

Application of the Lightgbm Algorithm in the Classification of Greenhouse Gas Emissions

“…Previous research was also conducted by Adnan et al (2023) on climate change data with the aim of conducting classification on the variable total greenhouse gas emissions (kt of CO2 equivalent) using logistical regression. The results obtained from the study resulted in a good accuracy rate of 87.60%.…”