@thesis{thesis,
		author={Abdiansah  Abdiansah and KRISYANI  LARASHATI and Utami  Alvi Syahrini},
		title ={ANALISIS SENTIMEN PEMBERLAKUAN PEMBATASAN KEGIATAN MASYARAKAT (PPKM) DI TWITTER MENGGUNAKAN METODE LONG SHORT-TERM MEMORY},
		year={2023},
		url={http://repository.unsri.ac.id/102054/},
		abstract={CARE is a policy made by the government to deal with the Covid-19 pandemic that occurred in Indonesia. Twitter as one of the social networking sites is also not spared from the discussion on the CARE topic. Various public opinions regarding CARE on Twitter contain pros and cons because CARE has made a sudden change in people's lives. This study aims to analyze public sentiment towards CARE policies that have been made. Sentiment analysis uses the Long Short-Term Memory algorithm to classify sentiment into negative, neutral, or positive classes. The study used 12.959 data obtained from the results of scraping. The result of this study is a system that can classify sentiment. The level of accuracy obtained is 82% with an average macro precision value of 0,78, an average macro recall value of 0,76 and an average macro F1-score value of 0,77; and the weighted average value of precision, the weighted average value of recall, and the weighted average value of F1-score each is 0,82 on the test data.}
		}