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Jointer - Journal of Informatics Engineering
Article . 2021 . Peer-reviewed
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Jointer - Journal of Informatics Engineering
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Analisis Sentimen New Normal Pada Masa Covid-19 Menggunakan Algoritma Naive Bayes Classifier

Authors: Shania Kaparang; Daniel Riano Kaparang; Vivi Pegie Rantung;

Analisis Sentimen New Normal Pada Masa Covid-19 Menggunakan Algoritma Naive Bayes Classifier

Abstract

Dampak dari pandemi covid-19 begitu besar sehingga pemerintah harus memiliki kebijakan agar dapat mengurangi dampaknya. Salah satu kebijakan pemerintah yaitu new normal yang mewajibkan seluruh masyarakat untuk pakai masker, jaga jarak dan cuci tangan. Dalam penerapannya tentu ada sentimen-sentimen baik positif maupun negatif yang diunggah ke dalam Twitter. Penelitian ini bertujuan untuk membuat pemodelan analisis sentimen masyarakat mengenai kebijakan new normal pemerintah pada masa pandemi covid-19 di Indonesia. Tahapan penelitian ini yakni crawling data, labeling, penghapusan data netral, preprocessing, pembagian training data dan testing data, pembuatan sistem klasifikasi naïve bayes, uji coba sistem dan visualisasi hasil penelitian dengan menggunakan wordcloud. Performa sistem klasifikasinya antara lain, tingkat akurasi 80,37%, presisi 87,38%, recall 82,57% dan f-measure 84,91%. Hasil dari penelitian ini yaitu 5194 tweets terklasifikasi sentimen positif dan 2908 tweets terklasifikasi sentiment negative, hal ini menunjukkan bahwa sentimen positif lebih banyak daripada sentimen negatif. Tetapi dari jumlahnya bisa dilihat bahwa perbandingannya tidak terlalu jauh antara sentimen positif dan sentimen negatif, artinya ada respon masyarakat yang masih kurang terhadap kebijakan pemerintah new normal pada masa pandemi.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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