Named Entity Recognition Using Conditional Random Fields for Flood Detection In Gerbang Kertosusila Based Twitter Data - Ulumiyyah, Ikrimatul; Rolliawati, Dwi; Izzuddin, Andik; Khalid, Khalid; Khunaefi, Anang; Ridwan, Mujib (Universitas Islam Negeri Sultan Syarif Kasim Riau, 2024)
The national strategic area Gerbang Kertosusila East Java should be aware of floods. One of the existing efforts is to place flood sensors at several flood-prone points. However, that way is constrained by the need for more equipment to handle the many needy areas. So it is necessary to develop technology for the dissemination of flood information. Dissemination of flood information was quickly obtained from social media Twitter. One way is to use Twitter's text data source for a Named Entity Recognition model to help detect flood events and their locations. The Named Entity Recognition (NER) model was constructed using the Conditional Random Fields (CRFs) method to achieve research objectives. This research adds slang word handling at the preprocessing stage to improve model performance and the use of the BIO format in the labeling process and POS Tagging in the Feature Extraction process. Evaluation results with five Kfolds, 80% training data, and 20% test data show that the NER CRFs model performs excellently with a Precision of 0.981, Recall of 0.926, and f-measure of 0.950 so that these results can help the community and government regarding the information on the distribution of floods.