Application of Named Entity Recognition via Twitter on SpaCy in Indonesian (Case Study : Power Failure in the Special Region of Yogyakarta)

Abstract

SpaCy is a tool that can efficiently handle Natural Language Processing (NLP) problems, one of which is Named Entity Recognition (NER). NER is used to extract and identify named entities in a text. However, so far SpaCy has not officially released the NER model pre-train for Indonesian. On the other hand, based on the 2019 PLN statistical report, the Province of D.I. Yogyakarta is a province that often experiences power failure and many complaints from the public are found on Twitter related to power failure that occur in the province. This is because there is no research on extracting information related to electrical disturbances and research on NER using SpaCy in Indonesian is still rare. So in this study, information extraction related to power failure in the Province of D.I. will be carried out. Yogyakarta via twitter using Indonesian SpaCy. This study produces good performance results with 95.52% precision calculation, 93.27% recall, and 94.38% f1-score. Then, mapping is carried out based on the location entities contained in tweets related to electrical disturbances. From this process, it was found that the highest number of locations mentioned in the tweet related to power failure came from Sleman Regency, while the lowest number came from Gunung Kidul Regency. Then, the month that experienced the most power failure was March 2020, while the month that experienced the least amount of electricity was July 2020.