BIDIRECTIONAL LSTM-CNNs UNTUK EKSTRAKSI ENTITY LOKASI KEBAKARAN PADA BERITA ONLINE BERBAHASA INDONESIA - Alif Andika Putra; Robert Kurniawan (Seminar Nasional Official Statistics, 2020)
DKI Jakarta Province is one of the areas prone to fire. BPBD DKI Jakarta as a Disaster Management Institution has one mission which is to increase the preparedness of the people of Jakarta for disasters, one of which is the fire disaster. Increased preparedness for fire disasters can be done by presenting information about locations prone to fire. BPBD DKI Jakarta in this case can utilize the development of information and communication technology, such as the internet as an information resource. Dissemination of information through the internet one of which is published in the form of an online news web. The information contained in online news articles can be used as a source of information in obtaining data. A series of processes is needed to be able to extract information contained in online news articles. In this study, information contraction in online news articles is done by classifying entities into certain classes using Name Entity Recognition (NER) with the deep learning hybrid network approach model of Bidirectional LSTM-CNNs (BLSTM-CNNs). This study shows the NER model with BLSTM-CNNs has good performance based on the results of F1-score, precision and recall calculation. Then, mapping is done based on the location entity contained in the online news article classification results using the NER model with BLSTM-CNNs.