ANALISIS SENTIMEN PENGGUNA TWITER TERHADAP METODE PEMBELAJARAN JARAK JAUH MENGGUNAKAN ALGORITMA NAÏVE BAYES

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dc.contributor.authorFitriani, Diah
dc.contributor.supervisorElfizar, Elfizar
dc.date.accessioned2022-07-18T08:24:02Z
dc.date.available2022-07-18T08:24:02Z
dc.date.issued2022-01
dc.description.abstractThe Covid-19 pandemic has caused various impacts, one of which is on educational activities. The Indonesian government decided to implement learning by the Distance Learning method. This has become a hot topic of discussion on Twitter. The circulated tweets contain public opinions on the implementation of Distance Learning. This study aims to classify sentiments to see the public's view of the Distance Learning implementation. Data used in this study is obtained by using the keywords of online lecture, networked lecture, online school, and networked school from October to November 2020. The sentiment is classified into two classes: positive and negative. The classification process is carried out by the pre-processing data and word weighting using the TF/IDF technique. Data classification is done by using the Naïve Bayes Multinomial algorithm and evaluation of the algorithm is done by using the confusion matrix method. The final result of the classification process yields a good accuracy rate of 83%, precision of 33.33% and recall of 6.25%. The results show that netizens do not fully agree with the implementation of Distance Learning. It is showed by negative sentiments are 97% and positive sentiments are 3%.en_US
dc.description.sponsorshipFakultas Matematika dan Ilmu Pengetahuan Alamen_US
dc.identifier.citationPerpustakaanen_US
dc.identifier.issnElfitra
dc.identifier.urihttps://repository.unri.ac.id/handle/123456789/10563
dc.language.isoenen_US
dc.publisherElfitraen_US
dc.subjectDistance Learningen_US
dc.subjectNaïve Bayesen_US
dc.subjectText Miningen_US
dc.subjectTF/IDFen_US
dc.titleANALISIS SENTIMEN PENGGUNA TWITER TERHADAP METODE PEMBELAJARAN JARAK JAUH MENGGUNAKAN ALGORITMA NAÏVE BAYESen_US
dc.typeArticleen_US

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