<![CDATA[The demand for goods delivery services is increasing along with the widespread use of e-commerce platforms for buying and selling. One of the popular and frequently used delivery service providers is J&T Express. Until now, J&T has had a wide service coverage. However, various customers also have complaints that are often conveyed through social media X. For this reason, this study conducted a sentiment analysis of J&T Express user opinions on social media X using the Naïve Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. Data collection was carried out through scraping over a time span from January 1, 2023 to December 1, 2024, resulting in a total of 1,000 data points. The modeling results show that the NBC algorithm outperforms KNN, achieving an accuracy of 72.30%, a precision of 74.76%, and a recall of 72.30%. Meanwhile, the KNN algorithm with the best parameters (K = 9) only has an accuracy of 67.29%, precision of 69.46%, and recall of 67.29%. Then the results of the analysis show that J&T user opinions are dominated by negative sentiment (42.20%), followed by positive sentiment (38.70%) and neutral sentiment (19.10%). Further analysis based on five variables was also conducted and an understanding of J&T's weaknesses, namely in the service aspect, with the highest negative sentiment (21.0%). On the other hand, the user experience aspect is an advantage with the most positive sentiment (16.8%). The data visualization results also indicate that there are dominant customer complaints about the delay in the delivery process. However, customers also appreciate the speed and security of the delivery of goods. These findings provide valuable insights for J&T Express to conduct evaluations and improvements, especially in the service aspect, to improve overall customer satisfaction and experience.]]>
