<![CDATA[The number of internet users in Indonesia is increasing every year, making it the fastest-growing country in the world, next only to China, India and the United States. In 2017, in Indonesia, the digital economy sector had a high impact on GDP, showing a figure of 7.3%, while the total economic development only reached 5.1%. Traveloka appeared in 2012 and has grown rapidly to be classified as the most superior travel application in Southeast Asia. As applied by the Traveloka application, it applies data scraping to collect 5000 review data from the intended platform. With the increase of Traveloka app user reviews on Playstore, the main challenge is to classify the sentiment of the reviews automatically and accurately. The purpose of this research is to find out the extent of user assessment of the Traveloka application. The results show that the model has an Accuracy of 0.91, indicating that 91% of the total data was predicted correctly. The model'sF1 Score of 0.90 reflects the optimal balance between Precision and Recall, indicating that the model is not only correct in predicting positive results, but also able to capture almost all positive examples. Precision of 0.92 indicates a high level of accuracy in positive predictions, while Recall of 0.88 indicates that the model's ability to detect all positive data is very good. In this analysis, out of the 940 data used, 250 True Positive (TP), 18 False Positive (FP), 608 True Negative (TN) and 64 False Negative (FN) were found, with an 80:20 data split. The findings show that the model can predict most of the data accurately, despite some errors in positive and negative classification. These results indicate that the model has high effectiveness in the identification and prediction of positive data, providing a strong basis for further applications in data analysis.]]>
