Analisis Sentimen Mengenai Vaksin Sinovac di Media Sosial Twitter Menggunakan Metode NaÃ¯ve bayes Classification

Aldisa, Rima Tamara; Azizah, Azizah; Abdullah, Mohammad Aldinugroho

doi:10.35870/jtik.v6i3.479

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Telah dilakukan juga penelitian di tahun 2021 juga yang membahas tentang analisis sentimen mengenai vaksin yang berjenis Sinovac dengan data yang berasal dari media sosial twitter [11]. Dalam penelitian ini, data diambil menggunakan metode crawling dengan python.…”

Section: Pendahuluanunclassified

Perbandingan Metode Naïve Bayes dan Support Vector Machine Untuk Analisis Sentimen Terhadap Vaksin Astrazeneca di Twitter

Indriyani¹,

Paradise²,

Wibowo³

2022

mib

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The implementation of Covid-19 vaccination in Indonesia turned out to have various pro and contra opinions from the public. The discovery of disinformation and misinformation about vaccines spread through social media content affects a person's absorption of information so which leads to vaccine delays. When in fact, vaccination is one of the biggest and most effective contributions to preventing the Covid-19 pandemic. Astrazeneca is one of the vaccines provided by the Indonesian government. This vaccine used to be controversial amongst the public regarding its halalness and the safety of the vaccine because of the issue of the said vaccine containing swine trypsin. Nowadays Twitter has become a place for users to express their concerns and opinion regarding the Covid-19 vaccine. Data obtained from Twitter will be useful if it is analyzed, one of which is sentiment analysis. In this study, data collection was carried out using the snscrape library with a total of 3105 tweets obtained from the period May to June 31, 2021. The dataset that has been collected is then preprocessed to optimize the data. After passing the preprocessing stage, the data was labeled as tweet class using a lexicon-based dictionary which resulted in 1275 tweets with positive opinin labels and 1830 tweets labeled as negative opinion. The aim of this study is to examines the performance of Naïve Bayes and Support Vector Machine with adding the weighting method TF-IDF (Term Frequency – Inverse Document Frequency). The evaluation results show that the Support Vector Machine has a greater accuracy, precision, recall and f1-score of 87.27%, 90.41%, 77,34% and 83.37% compared to Naïve Bayes which has an accuracy, precision, recall and f1- of 76.81%, 72.40%, 70.70% and 71.52%.

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Section: Pendahuluanunclassified

Perbandingan Metode Naïve Bayes dan Support Vector Machine Untuk Analisis Sentimen Terhadap Vaksin Astrazeneca di Twitter

Indriyani¹,

Paradise²,

Wibowo³

2022

mib

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“…Hasil penelitiannya adalah sentimen positif lebih besar dibandingkan sentimen negatif dengan tingkat akurasi 92,96% [6]. Penelitian serupa juga dilakukan Rima Tamara, dkk dengan hasil yang kurang lebih sama [7]. Zulfikar dan Nila juga melakukan penelitian serupa dengan menggunakan algoritma Naïve Bayes.…”

Section: Dasar Teoriunclassified

Analisis Sentimen Vaksinasi Booster Covid-19 pada Platform Twitter Menggunakan Metode Naïve Bayes

Anggraeni

2022

Expert J. Manaj. Sist. Inf. dan Teknol.

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Since the end of 2019, the Covid-19 virus hit the whole world, including in Indonesia. One of the efforts to deal with the Covid-19 virus is vaccination. In Indonesia, the government requires people to vaccinate 3 times, that are First Vaccination, Second Vaccination, and Booster Vaccination. The public's response to the booster vaccine are varies. This study aims to reveal public sentiment towards booster vaccine activities. The research was conducted by collecting tweet data from the Twitter platform. The research was conducted by collecting data tweets from Twitter. The method used is the Naïve Bayes Classifier because the method is simple, the process is fast, and it has a fairly high level of confidence. In this method, public sentiment is divided into three, that are positive, neutral, and negative. The results showed that most people responded positively to this booster vaccine activity with a value of 56.8%, neutral as much as 39.9%, and negative as much as 3.3% with an accuracy rate of 86%.

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Sentiment Analysis of Public on The COVID-19 (Corona Virus Disease 2019) Vaccination Moderna and Sinovac Vaccine Using Naïve Bayes

Afni

Silmina²

2022

Senatik

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Analisis Sentimen Mengenai Vaksin Sinovac di Media Sosial Twitter Menggunakan Metode NaÃ¯ve bayes Classification

Cited by 3 publications

References 4 publications

Perbandingan Metode Naïve Bayes dan Support Vector Machine Untuk Analisis Sentimen Terhadap Vaksin Astrazeneca di Twitter

Perbandingan Metode Naïve Bayes dan Support Vector Machine Untuk Analisis Sentimen Terhadap Vaksin Astrazeneca di Twitter

Analisis Sentimen Vaksinasi Booster Covid-19 pada Platform Twitter Menggunakan Metode Naïve Bayes

Sentiment Analysis of Public on The COVID-19 (Corona Virus Disease 2019) Vaccination Moderna and Sinovac Vaccine Using Naïve Bayes

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