Implementation of Text Association Rules about Terrorism on Twitter in Indonesia

Along with globalization's positive effects, there are many negative consequences. Cybercrime, cyber-war, and cyberterrorism have entered social media platforms like Twitter. Therefore, identifying opinion patterns in social media is an important research task to understand what types of conversations about terrorism case patterns occur. This study will add to the knowledge of terrorism concerns in Indonesian social media. Then we might be willing to seek guidance way earlier and avoid any conflict. We attempt to analyze Twitter data with the keyword “terrorism” based on the existing problems. We want to prove that using the FP-Growth algorithm can generate rules from terrorism-related tweets. The FP-Growth algorithm generates 75 association rules from Tweet data which was collected on November 20, 2021, with minimum support of 0.05 and minimum confidence of 0.9.