Predicting The Quality of Red Wine and White Wine Using Data Mining

Ni Wayan Priscila Yuni Praditya (Universitas Indo Global Mandiri)
Noor Akhmad Setiawan (Universitas Gadjah Mada, Yogyakarta, Indonesia)
Fery Antony (Universitas Indo Global Mandiri)

Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)
Vol 4, No 2 (2023)

In business intelligence or artificial intelligence, data mining is a technique that can classify and cluster data based on the nature and correlation of the data set used. in data mining, several methods can be used, such as C45, K-Means, Apriori Decision Tree, KNN, LSTM, Naive Bayesian, etc. This research utilizes the Decision Tree method which aims to classify the quality of red wine and white wine. The results of this study indicate that the prediction of red wine has a precision of 61.1%, recall of 60.7%, f-measure of 60.3%, and an average accuracy of 60.7% while white wine has a precision of 58.2%, recall of 58.7%, f-measure 58.4%, and 58.7% accuracy. The method used in this study also shows that Decision Tree can outperform other methods such as Lib-SVM, BayesNet, and Multi Perceptron.