Online transportation is a transportation innovation that has emerged along with the development of online-based applications that provide many features and conveniences. In its development, many users wrote their responses to the application on social media such as twitter. Many opinions and responses are directly conveyed by users of online transportation modes to their official accounts. The responses given by these users are very large and can be used as sentiment analysis on online transportation. However, the analysis process cannot be done manually. Therefore, we need a system that can help analyze user responses on Twitter automatically. In this study, a sentiment analysis system was built for online transportation in Indonesia using the ensemble stacking algorithm, which will simplify and increase the accuracy of the sentiment analysis. Ensemble stacking is a solution for advanced machine learning methods that can improve the performance of the base classifier. The system built on ensemble stacking uses three base classifiers, namely SVM kernel RBF, SVM linear kernel, and logistic regression. The best accuracy result on the gojek dataset is 88%, and the best F1 score is 87%. Ensemble Stacking which is applied to the research that the author conducted on online transportation sentiment analysis on twitter, obtained better accuracy than the base classifier used.