CD Skripsi

Implementasi Naïve Bayes Classifier Pada Twitter Dalam Analisis Sentimen Terhadap Varian Baru Corona Virus Omicron

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ABSTRACT
Omicron is a new variant of the Corona virus that was first reported to the World
Health Organization (WHO) from South Africa on November 24, 2021. The amount
of information related to the Omicron virus that is spread in the form of text on the
internet and on social media sites like Twitter is of interest to researchers. This
study aims to determine public sentiment from Twitter users about the omicron
virus, then determine sentiment classification using the Nave Bayes Classifier and
calculate the level of accuracy of the method in sentiment analysis. The data used
is 1350 data points using two data testing scenarios, namely the first scenario, Split
Validation, using a data ratio of 70%:30%, 80%:20%, and 90%:10%. The second
scenario uses cross-validation, divided into 10 tests. Sentiment classifiers are
divided into three classes: neutral, fearful, and fearless. The classification process
is carried out by means of data pre-processing and weighting using the TF-IDF
technique. The final results of the classification process get the highest accuracy
values of 85%, 81% precision, and 86% recall in the split validation test with a
90:10 data division. Testing data with cross-validation resulted in the highest
accuracy of 93,1% accuracy, 36,6% precision, and 98% recall, with a fold value of
1.
Keywords : Sentiment Analysis, Cross Validation, Fold, Split Validation, Naïve
Bayes Classifier, Omicron, TF-IDF, Data Testing, Twitter, Virus.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

1803111059

Penerbit

Pekanbaru : Universitas Riau – FMIPA – Sistem Informasi., 2023

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

1803111059

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

SISTEM INFORMASI

Info Detail Spesifik

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Pernyataan Tanggungjawab

PUJA

Versi lain/terkait

Lampiran Berkas

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