<![CDATA[Sentiment analysis on public opinion regarding the shopee food application is an interesting topic in the context of evaluating service quality in the shopee food application. In this digital era, user opinion has a very important role in shaping public perception of the application. Therefore, sentiment analysis is needed to understand user opinion about the shopee food application. This study uses Decision Tree C4.5 to analyze public sentiment on the use of the Shopee Food application on Twitter users. However, beforehand it is necessary to overcome the problem of data imbalance which is common in datasets, where the number of positive, negative, and neutral sentiments is not balanced. To overcome this problem, three different techniques are used, namely SMOTE, undersampling, and a combination of oversampling and undersampling. The results of this study indicate that the SMOTE technique provides better results in overcoming data imbalances and increasing prediction accuracy. With an accuracy of 0.88. the SMOTE technique can provide more accurate sentiment predictions than the undersampling technique and the combination of oversampling and undersampling. This is because SMOTE can synthetically expand the number of minority samples, thereby preventing the loss of information and maintaining variation in the dataset. In conclusion, sentiment analysis on the Shopee Food application on Google Play using the Decision Tree C4.5 algorithm and the SMOTE technique can overcome data imbalances with a prediction accuracy of 0.88. This technique is more efficient than the undersampling technique and the combination of oversampling and undersampling. These results can provide developers with valuable insights to improve app quality and user satisfaction.]]>
Copyrights © 2023
