Comparison of Support Vector Machine and XGBoost Algorithms in Sentiment Analysis of Visitor Reviews of Baturraden Tourism Forest

The Google Maps platform provides a platform for visitors to express their opinions through reviews. This study aims to compare the performance of the Support Vector Machine and XGBoost algorithms in sentiment analysis of Baturraden Tourism Park visitor reviews. Data were collected using scraping techniques and obtained 4,096 reviews. After going through preprocessing stages including cleaning, tokenization, normalization, stopword removal, and stemming, the data used in the analysis process amounted to 2,912 reviews. Word weighting was carried out using the TF-IDF method, and the SMOTE technique was applied to address class imbalance. The results showed that the Support Vector Machine algorithm performed better than XGBoost with an accuracy rate of 94.52% before SMOTE and 94.86% after SMOTE, while XGBoost obtained an accuracy of 92.80% before SMOTE and 93.15% after SMOTE. These findings indicate that the Support Vector Machine is more effective in classifying positive and negative sentiments. This study is expected to contribute to the application of machine learning methods to understand visitor opinions on the Google Maps platform. Especially in the context of tourist attractions.