Sosial Media Analisis Berbasis NLP Untuk Mempercepat Tanggap Bencana Banjir - Marleny, Finki Dona; Mambang (LPPM POLITEKNIK LP3I BANDUNG, 2022)
Flood disasters that are monitored in real-time on social media can be seen to report directly the condition of the affected areas. Areas that have been warned to be affected by the floods were informed via social media. Surrounding areas that are likely to be affected can be more vigilant by directly speeding up information and getting responses from social media users who are around flood-prone areas. The research aims to provide visualization models that can accelerate flood response information from disaster management, track disasters with increasing vigilance, and accelerate flood disaster recovery with analysis on social media. The approach used with Natural Language Processing (NLP), a data source derived from posts on Instagram is taken for analytical materials. Data sources from Instagram with flood hashtags in Kalimantan are used by using the Natural Language Processing (NLP) process stage to get core information visualizations to speed up flood response information. Visualization of social media data information used based on extracting information from Instagram posts, responses, and hashtags to speed up information provides troubleshooting and the importance of speeding up flood response information. The results of data visualization can accelerate disaster response information to increase awareness of the condition of the surrounding area that can be affected by floods, it can be seen that the amount of data on the hashtag provides data visualization information in accelerating flood disaster management from disaster tracking to disaster recovery.