X or Twitter is one of the largest social media platforms in the world, which allows users to share information in the form of tweets. However, with its popularity, Twitter has also become a major target for spammers who spread unwanted content. This research examines the application of regular expressions (regex) and string matching algorithms in detecting spam tweets. The algorithms used include Brute Force, Knuth-Morris-Pratt (KMP), and Boyer-Moore to match text patterns with tweet data. The experimental results show that the Boyer-Moore (BM) algorithm provides the highest efficiency. By implementing this strategy, it is hoped that it can help reduce the spread of spam on the Twitter platform and improve the overall user experience.

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