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All Journal Formosa Journal of Multidisciplinary Research (FJMR)
Vicky Caren
Universitas Pelita Harapan, Kampus Medan
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Social Sciences

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Modeling Red Wine Quality Based on Physicochemical Tests: A Data Mining Approach Maeve Eunicia; Richie Skyszygfrid; Tiara Vitri; Vicky Caren
Formosa Journal of Multidisciplinary Research Vol. 1 No. 1 (2022): May, 2022
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (471.06 KB) | DOI: 10.55927/fjmr.v1i1.414

Abstract

Classification of the quality of red wine is done in the hope of making it easier to assess the quality of red wine. Data used for this research is the wine quality data set with 4898 number of instances, obtained from UCI machine learning repository. Classification of the quality of red wine this study was carried out by comparing the three algorithms of data mining, that is random forest, naive bayes and generalized linear model. From the results of this study comparing the three algorithms, the generalized linear model showed the highest accuracy among the other algorithms. It was tested with a generalized linear model with 68.75% accuracy results, this algorithm is ideal for classifying the quality of red wine. In addition, a secondary random forest gives 67.81% accuracy results, while Naive Bayes gives 61.25% accuracy results. Studies conducted to classify the quality of red wine based on its composition use a generalized linear model for the optimal algorithm.
Co-Authors Maeve Eunicia Richie Skyszygfrid Tiara Vitri