Skripsi

KLASIFIKASI SENTIMEN TERHADAP DATA TEXT JEJARING SOSIAL DENGAN TOPIK VAKSIN COVID-19 MENGGUNAKAN NAÏVE BAYES CLASSIFIER

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Penilaian

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The coronavirus vaccine which will be distributed to citizens in order to stop this pandemic that has been announced by the government in August 2020, in this time the government's policy has triggered many public reactions, both negative and positive sentiments towards the vaccine that the government has just approved. With the variety of public sentiments towards the Covid-19 vaccine, the classification of public sentiment towards the vaccine is considered necessary because of the high enthusiasm of the community in welcoming the vaccine that will be launched by the government. This sentiment classification is made possible by the data that has been collected on a social network that contains a lot of public sentiment, especially regarding the topic of the Covid-19 Vaccine, by using Natural Language Processing (NLP), it is possible to classify sentiments in the form of text data, and with using the Naïve Bayes Classifier as the basis for the classifier used, the community sentiment data will be classified into several parts. While the 80% test data has the lowest performance where the precision value is 84.25%, recall is 80.94%, and F1-score is 82.12%. The Naïve Bayes method used in this study is able to classify text data well, so that the sentiment on the topic of the Covid-19 vaccine can be clarified by the system. The results of sentiment predictions made with the Naïve Bayes algorithm can be used as reference material for the government in controlling the contribution of vaccines to the community.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002184	T52776	T527762021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T527762021

Publisher

Inderalaya : Fakultas Ilmu Komputer, Prodi Sistem Informasi., 2021

Collation

xvi, 65 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.660 7

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Metode Transmisi Data
Prodi Sistem Informasi

Specific Detail Info

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Statement of Responsibility

MURZ

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