Detecting Trending Topics Using Soft Frequent Pattern Mining (SFPM) on Indonesian Language Tweets Related to Earthquake News - Indra, Indra; Prakoso, M Syawaladi Kukuh; Rosul, Mekar Bunga Allamanda; Mufti, Mufti (Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI, 2025)
Social media users frequently post updates regarding ongoing natural disasters, including specific details and locations. These posts are crucial for real-time insights into such events; however, their informal tone and use of slang can make them difficult to utilize effectively. This study employs Soft Frequent Pattern Mining to detect trending earthquake topics in Indonesia using a specific Indonesian language dataset from X. The three-week testing period revealed varied performances: the first week showed a topic recall of 0.57, the second improved to 0.72, and the third drastically decreased to 0.28, indicating a temporary lack of significant trending topics. Averaging topic recall at 0.52, keyword precision at 0.34, and keyword recall at 0.45, the results highlight substantial room for improvements. This underlines the importance of methodological optimizations in future research to enhance the system’s effectiveness in identifying and validating widely discussed issues.