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All Journal Infolitika Journal of Data Science
Huzair Saputra
Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Enhancing the Red Wine Quality Classification Using Ensemble Voting Classifiers Deny Joefakri Iwa Supriatna; Huzair Saputra; Khaidir Hasan
Infolitika Journal of Data Science Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i2.95

Abstract

This study introduces an ensemble voting classifier for red wine quality classification using machine learning algorithms. Wine quality assessment, traditionally reliant on subjective expert evaluations, is addressed through data-driven methodologies. The dataset comprises physicochemical attributes and quality ratings of red wines. Results reveal individual models with accuracy ranging from 0.816 to 0.873, while the ensemble approach significantly enhances accuracy. The combination of Random Forest and XGBoost achieves an accuracy of 0.885, demonstrating its potential in red wine quality assessment. In conclusion, this study showcases the potential of machine learning in enhancing the classification of red wine quality, offering a more objective and precise alternative to traditional sensory evaluation. The ensemble voting classifier, especially when combining Random Forest and XGBoost, provides a robust solution for this task, improving the accuracy of wine quality assessments.
Co-Authors Deny Joefakri Iwa Supriatna Khaidir Hasan