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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IKRA-ITH Informatika : Jurnal Komputer dan Informatika
Dinar Ajeng Kristiyanti
Universitas Multimedia Nusantara
Computer Science & IT Engineering

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Systematic Literature Review: Analisa Sentimen Penerimaan Masyarakat Terhadap Jenis Vaksin Covid-19 Di Dunia Sri Hardani; Dinar Ajeng Kristiyanti
ikraith-informatika Vol 6 No 3 (2022): IKRAITH-INFORMATIKA Vol 6 No 3 November 2022
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v6i3.2204

Abstract

Covid-19 menjadi hal yang banyak menyita perhatian dunia tiga tahun terakhir. Wabah yang dengancepat menyebar ke berbagai negara ini telah memakan jutaan korban dan memberikan dampak buruk diberbagai sektor. Berbagai upaya dilakukan guna menanggulangi wabah virus Covid-19, salah satunyadengan pemberian vaksin pada masyarakat. Namun ternyata solusi ini tidak langsung mendapat responpositif dari masyarakat. Adanya efek samping dan berbagai kejadian yang mengiringi pelaksanaan programvaksin Covid-19, memicu warga masyarakat memberikan opini yang beragam terkait penggunaan vaksinCovid-19. Penelitian ini menyampaikan hasil tinjauan literatur sistematis terkait opini masyarakat terhadappenggunaan vaksin Covid-19. Penelitian ini merupakan studi litratur yang menggunakan literasi terbitantahun 2019–2022 dimana Covid-19 melanda dunia. Tahapan yang dilakukan dalam penelitian ini antaralain perencanaan review, implementasi protokol review, dan penyampaian hasil review. Tujuan daripenelitian ini adalah memberikan gambaran umum mengenai teknologi yang banyak digunakan dalamanalisa sentimen masyarakat terhadap penggunaan vaksin Covid-19 baik metode, algoritma ataupun jenismachine learning, negara mana yang banyak dijadikan objek penelitian, serta jenis vaksin apa yang banyakmendapat perhatian masyarakat. Penelitian ini diharapkan mampu membantu penelitian yang akan datanguntuk mengembangkan metode dan teknik baru sehingga memberikan hasil yang lebih akurat.
Sentiment Analysis of Public Acceptance of Covid-19 Vaccines Types in Indonesia using Naïve Bayes, Support Vector Machine, and Long Short-Term Memory (LSTM) Dinar Ajeng Kristiyanti; Sri Hardani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4737

Abstract

The Covid-19 vaccination is a government program during the pandemic to create herd immunity so that people become more productive in their activities. In Indonesia, the Covid-19 vaccination campaign employs a range of vaccines and has sparked a range of responses from the public on social media, particularly Twitter. Users can tweet and communicate with one another on the social networking site Twitter. This study uses a Sentiment Analysis technique using the Nave Bayes (NB), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) algorithms to conduct a sentiment analysis of public acceptance of the type of Covid-19 vaccine used in Indonesia using Twitter data. Various types of vaccines in Indonesia include Sinovac, Vaksin Covid-19 Bio Farma, AstraZeneca, Pfizer, Moderna, Sinopharm, Novavax, Sputnik-V, Janssen, Convidencia, Zifivax, often confuse the public in determining the objectivity of this opinion. In addition, theoretically, this study also seeks to contrast the NB, SVM, and LSTM algorithms with experimental techniques to obtain the best algorithm model. The stages of the research involved gathering information based on Twitter user opinions about the type of Covid-19 vaccine on Twitter from January 2021 to January 2022. The researcher used Indonesian language tweet data with the keywords #vaksincorona, #vaksincovid19, #vaksinasi, #ayovaksin, #lawancovid19, and #vaksinindonesia. Before modelling, the pre-processing stage consists of case folding, tokenizing, filtering, stemming, and word weighting using TF-IDF. After that, model testing was carried out using Cross Validation with the Python programming language, and evaluation and validation of the test results using the Confusion Matrix. The results showed that the accuracy score of the SVM method for the best model was 84.89%, while for the Naïve Bayes and LSTM algorithms, they were 84.65% and 82.97%, respectively.
Co-Authors Sri Hardani