Prediksi Kualitas Red Wine dan White Wine Menggunakan Data Mining
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Abstract
Data mining is a technique used in business intelligence or artificial intelligence capable of classifying and clustering data based on the nature and correlation of the data sets used. The methods commonly used in data mining are C45, K-Means, Apriori Decision Tree, KNN, LSTM, Naive Bayesian, etc. In this study, the method used is 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 the Decision Tree can outperform other previously applied methods, namely Lib-SVM, BayesNet, and Multi Perceptron.
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