Analisis Sentimen Masyarakat terhadap Kebijakan Vaksinasi Covid-19 di Indonesia pada Twitter Menggunakan Algoritma LSTM

Article Sidebar

PDF (Indonesian)
Published: Dec 31, 2022
DOI: https://doi.org/10.17933/iptekkom.24.2.2022.161-172
Keywords:
Covid-19, LSTM (Long Short-Term Memory), Sentiment Analysis, Twitter, Vaccination
Dimensions
Altmetrics
Statistics
Read Counter : 695
Download : 550
Crossmark/ Data Version

Main Article Content

Laila Marifatul Azizah
Universitas Muhammadiyah Yogyakarta
Dimas Bagas Ajipratama
Universitas Muhammadiyah Yogyakarta
Nisrina Akbar Rizky Putri
Cahya Damarjati
Universitas Muhammadiyah Yogyakarta

Abstract

Indonesia was shocked by the emergence of the first case of Covid-19 in March 2020. The Covid-19 virus can be fought with herd immunity, namely by vaccinating. On December 16, 2020, President Joko Widodo announced that he would provide the Covid-19 vaccine to the people of Indonesia. The information received various responses from the public. One of them through twitter. There are opinions that support and there are also those who reject vaccination. To find out the opinion of public sentiment regarding vaccination, a sentiment analysis process is carried out using an algorithm that aims to assist the sentiment analysis process with quite a lot of data. In this study, the sentiment analysis process uses one of the deep learning methods, namely LSTM (Long Short-Term Memory). The results of this study tend to support the vaccination program by producing 79% positive tweets, 13% neutral tweets and 8% negative tweets and getting a model accuracy of 71% using parameters of 15 epochs, 64 batch sizes and a comparison of training data and test data of 9:1 ​​(3600:400).

Article Details

Issue
Vol. 24 No. 2 (2022): Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi)
Section
Articles
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Authors who publish with this journal agree to the following terms:

  1. Author (s) hold copyrights and retain copyrights of articles if the article is accepted for publishing.
  2. The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
  3. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  4. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
  5. The article and any associated published material are distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Public allowed to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material) this journal article content.

References

Aditiya, Piqih, Ultach Enri, and Iqbal Maulana. 2022. "Analisis Sentimen Ulasan Pengguna Aplikasi Myim3 Pada Situs Google Play Menggunakan Support Vector Machine." JURIKOM (Jurnal Riset Komputer) 1020−1028.

Aldisa, Rima Tamara, and Pandu Maulana. 2022. "Analisis Sentimen Opini Masyarakat Terhadap Vaksinasi Booster COVID19 Dengan Perbandingan Metode Naive Bayes, Decision Tree dan SVM." Building of Informatics, Technology and Science (BITS) 106−109.

Anggraini, Novita, Edi Surya Negara Harahap, and Tri Basuki Kurniawan. 2021. "Text Mining - Analisis Teks Terkait Isu Vaksinasi COVID-19 ." Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) 141 - 153 .

Caniago, Alfi, and Eko Hero. 2022. "Fenomena Mengunggah Film Pendek di Media Sosial pada Mahasiswa Komunikasi Universitas Islam Riau." Journal of Social Media and Message 24-35.

Hernikawati, Dewi. 2021. "Kecenderungan Tanggapan Masyarakat Terhadap Vaksin Sinovac Berdasarkan Lexicon Based Sentiment Analysis." Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) 21 - 31.

Ihsan, Miftahul, Benny Sukma Negara, and Surya Agustian. 2022. "LSTM (Long Short Term Memory) for Sentiment COVID-19 Vaccine Classification on Twitter." Jurnal Teknologi Informasi & Komunikasi Digital Zone.

Lestandy, Merinda, Abdurrahim, and Lailis Syafaah. 2021. "Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes." JURNAL RESTI (Rekayasa Sistem dan Teknologi Informasi) 802 - 808.

Lestari, Sri, and Sudin Saepudin. 2021. "ANALISIS SENTIMEN VAKSIN SINOVAC PADA TWITTER MENGGUNAKAN ALGORITMA NAIVE BAYES." SISMATIK (Seminar Nasional Sistem Informasi dan Manajemen Informatika) .

Nanda, Robbi, Elin Haerani, Siska Kurnia Gusti, and Siti Ramadhani. 2022. "Klasifikasi Berita Menggunakan Metode Support Vector Machine." Jurnal Nasional Komputasi dan Teknologi Informasi.

Nasruddin, Rindam, and Islamul Haq. 2020. "Pembatasan Sosial Berskala Besar (PSBB) dan Masyarakat Berpenghasilan Rendah." Jurnal Sosial & Budaya Syar-i 639-648.

Prijono, Benny. 2018. Pengenalan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU) – RNN Bagian 2. April 13. Benny Prijono.

Putra, Andreyana Pratama, Yuda Pratama, Eka Kharisma Krisnadi, Indah Purnamasari, and Dedi Dwi Saputra. 2022. "Text Mining untuk Sentimen Analisis dengan Metode Naïve Bayes, SMOTE, N-Gram dan AdaBoost Pada Twitter CommuterLine." Jurnal Sains Komputer & Informatika (J-SAKTI) 961 - 973.

Rachman, Fajar Fathur, and Setia Pramana. 2020. "Analisis Sentimen Pro dan Kontra Masyarakat Indonesia tentang Vaksin COVID-19 pada Media Sosial Twitter." Indonesian of Health Information Management Journal 100-109.

Rahman, Muhammad Zaini, Yuita Arum Sari, and Novanto Yudistira. 2021. "nalisis Sentimen TweetCOVID-19 menggunakan Word Embeddingdan Metode Long Short-Term Memory(LSTM)." Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer 5120-5127.

Roihan, Ahmad, Po Abas Sunarya, and Ageng Setiani Rafika. 2020. "Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper." IJCIT (Indonesian Journal on Computer and Information Technology) 75-82.

Rozaqi, Afid. 2022. "ANALISIS SENTIMEN VAKSINASI BOOSTER BERDASARKAN TWITTER MENGGUNAKAN ALGORITMA NAÏVE BAYESDAN K-NN." JURNAL SISTEM SIBER SOSIAL 01-09.

Sari, Mike Febri Mayang, and Dian Permata Sari. 2022. "Artificial Intelegence Perbandingan Algoritma Simple Hill Climbing Dan Steepest Ascent Hill Climbing Dalam Media Pembelajaran Alfabert." Jurnal Sains Komputer & Informatika (J-SAKTI).

Urva, Gellysa, Merina Pratiwi, and Amiroel Oemara Syarief. 2022. "Optimalisasi Media Sosial Sebagai Penunjang Digital Marketing." Jurnal Pengabdian Kepada Masyarakat 56-61.

Utomo, Dito Putro, and Mesran. 2020. "Analisis Komparasi Metode Klasifikasi Data Mining dan Reduksi Atribut Pada Data Set Penyakit Jantung." JURNAL MEDIA INFORMATIKA BUDIDARMA 437-444.

Widayat, Widi. 2021. "Analisis Sentimen Movie Review menggunakan Word2Vec dan Deep Learning." JURNAL MEDIA INFORMATIKA BUDIDARMA 1018-1026.