Utilization of geocoding for mapping infrastructure impacts and mobility due to floods in indonesia based on twitter analytics

Flooding, a frequent natural disaster in Indonesia, is caused by several factors such as high-intensity rainfall, climate change, inadequate drainage and urban infrastructure challenges, impacting communities, infrastructure and economic activities. The lack of accurate and centralized data hinders government efforts to identify affected areas and respond effectively. Named Entity Recognition (NER), a machine learning-based information extraction tool, offers the potential for geocoding flood-related data from social media, such as Twitter. The purpose of this research is to develop a Named Entity Recognition (NER)-based model to extract location information from Twitter and visualize flood impacts through geocoding. The method used is a combination of Qualitative Analysis with Machine Learning and Geospatial Analysis to assess flooding impacts using Twitter data. Initially, a qualitative analysis of tweets extracts flood-related keywords to identify patterns. Then, Named Entity Recognition (NER) identifies locations, which are converted into geographic coordinates through geocoding for map visualization. The results show that location extraction from flood-related tweets using the Named Entity Recognition (NER) model and geocoding produces very useful and accurate data. About 50% of the flood-related tweets included location tokens, which shows the importance of geographic information in understanding the impact of disasters. The location extraction process using the NER model proved to be effective, although there were some discrepancies between the extracted location tokens and the actual geographic data, especially at the more detailed location level. However, the evaluation results show that 99.5% of the extracted locations correspond to valid locations, especially in the Indonesian region. This shows that the use of the NER model and geocoding is highly effective in analyzing flood impacts and provides significant benefits in disaster management and geospatial analysis based on social media data.