Prediksi Kualitas Red Wine dan White Wine Menggunakan Data Mining

Main Article Content

Ni Wayan Priscila Praditya


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.

Article Details

Art Jun


J. T. Hardinata, H. Okprana, A. P. Windarto and W. Saputra, "Analisis Laju Pembelajaran dalam," Jurna Sains Komputer & Informatika (J-SAKTI), vol. 3 No 2, pp. 422-432, 2019.

S. Aich, A. A. Al-Absi, K. L. Hui and M. Sain, "Prediction of Quality for Different Type of Wine based on Different Feature Sets Using Supervised Machine Learning Techniques," ICACT Transactions on Advanced Communications Technology, vol. 7, no. 3, pp. 1122-1127, 2018.

S. Kumar, K. Agrawal and N. Mandan, "Red Wine Quality Prediction Using Machine Learning Techniques," in International Conference on Computer Communication and Informatics, Coimbatore, India, 2020.

S. Lee,, J. Park and . K. Kang, "Assessing wine quality using a decision tree," in IEEE International Symposium on Systems Engineering (ISSE), Rome, Italy, 2015.

Y. Gupta, "Selection of important features and predicting wine quality using machine learning techniques," in Procedia Computer Science, Kurukhshetra, 2018.

Y. Er and A. Atasoy, "The Classification of White Wine and Red Wine According to Their Physicochemical Qualities," in International Journal of Intelligent Systems and Applications in Engineering, Turkey, 2016.

R. Supriyadi, W. Gata, N. Maulidah and A. Fauzi, "Penerapan Algoritma Random Forest Untuk Menentukan Kualitas Anggur Merah," Jurnal Ilmiah Ekonomi dan Bisnis, vol. 13 No.2, pp. 67-75, 2020.

D. Radosavljević, "A Data Mining Approach to Wine Quality Prediction," in International Scientific Conference, Gabrovo, 2019.

R. Croce, C. Malegori, P. Oliveri, . I. Medici and A. Cavaglioni, "Prediction of quality parameters in straw wine by means of FT-IR spectroscopy combined with multivariate data processing," in Food Chemistry, Italy, 2020.

E. N. R. Khakim, A. Hermawan and D. Avianto, "Implementasi Correlation Matrix Pada Klasifikasi Dataset Wine," JIKO (Jurnal Informatika dan Komputer), vol. 2, pp. 158-166, 2023.