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


  • Arisman Adnan Department of Statistics Universitas Riau, Indonesia
  • Anne Mudya Yolanda Universitas Riau
  • Gustriza Erda Department of Statistics Universitas Riau, Indonesia
  • Noor Ell Goldameir Department of Statistics Universitas Riau, Indonesia
  • Zul Indra Department of Information System Universitas Riau, Indonesia




Classification, climate change, logistic regression, machine learning, transformation data


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

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How to Cite

Adnan, A. ., Yolanda, A. M., Erda, G. ., Goldameir, N. E. ., & Indra, Z. . (2023). The Comparison of Accuracy on Classification Climate Change Data with Logistic Regression. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(1), 56-61.