Sentiment Analysis of Covid-19 Vaccination on Twitter  Using Classification Algorithms based on PSO

Fuji Astuti; Resi Taufan

doi:10.32520/stmsi.v11i2.1737

Sentiment Analysis of Covid-19 Vaccination on Twitter Using Classification Algorithms based on PSO

Fuji Astuti, Resi Taufan

Abstract

Twitter social media is widely used by internet users to provide opinions on an event. In 2021, the opinion about the Covid-19 vaccination was widely discussed by the Indonesian people on social media. Togetherness of opinion can be categorized as positive sentiment and negative sentiment. To categorize negative and positive sentiments, data mining processes can be used. This study discusses the sentiment of Covid-19 vaccination using classification. Data from Twitter is collected in a crawling process, then labeled into two classes, namely positive and negative sentiments. After labeling (polarity) the next stage is data preprocessing which consists of transform case, annotation removal, tokenizing and stemming. The classification algorithm used is Naïve Bayes (NB) and Support Vector Machine (SVM) and then compared with the classification algorithm using Particle Swarm Optimization (PSO). Tests were carried out using k-Fold Cross Validation to obtain accuracy values, Confusion Matrix tables and Area Under Curve. The test results on the classification using PSO are better than those without using PSO. The results of the accuracy of the NB and SVM algorithms are 64.04% and 72.55%, while the accuracy results after PSO on the SVM and NB algorithms are 70.43% and 76.38%, respectively.

Full Text:

PDF

References

T. Krisdiyanto, E. Maricha, and O. Nurharyanto, “Analisis Sentimen Opini Masyarakat Indonesia Terhadap Kebijakan PPKM pada Media Sosial Twitter Menggunakan Naïve Bayes Clasifiers,” vol. 7, no. 1, pp. 32–37, 2021.

S. Stieglitz, M. Mirbabaie, B. Ross, and C. Neuberger, “International Journal of Information Management Social media analytics – Challenges in topic discovery , data collection , and data preparation,” Int. J. Inf. Manage., vol. 39, no. December 2017, pp. 156–168, 2018, doi: 10.1016/j.ijinfomgt.2017.12.002.

S. Nugroho and I. Hidayat, “Efektivitas Dan Keamanan Vaksin Covid-19 : Studi Refrensi,” J. Keperawatan Prof., vol. 9, 2021.

N. P. Astuti, E. G. Z. Nugroho, J. C. Lattu, I. R. Potempu, and D. A. Swandana, “Persepsi Masyarakat terhadap Penerimaan Vaksinasi Covid-19: Literature Review,” J. Keperawatan, vol. 13, no. 3, pp. 569–580, 2021, doi: 10.32583/keperawatan.v13i3.1363.

R. Yanuarti, “Jurnal Sistem dan Teknologi Informasi Analisis Media Sosial Twitter Terhadap Topik Vaksinasi Covid-19,” vol. 6, no. 2, pp. 121–130, 2021.

F. Zamachsari, G. V. Saragih, Susafa’ati, and W. Gata, “Analisis Sentimen Pemindahan Ibu Kota Negara dengan Feature Selection,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 3, pp. 504–512, 2020.

P. Arsi, R. Wahyudi, and R. Waluyo, “Optimasi SVM Berbasis PSO pada Analisis Sentimen Wacana Pindah Ibu Kota Indonesia,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 231–237, 2021, doi: 10.29207/resti.v5i2.2698.

N. Hardi, Y. Alkahfi, P. Handayani, W. Gata, and M. R. Firdaus, “Analisis Sentimen Physical Distancing pada Twitter Menggunakan Text Mining dengan Algoritma Naive Bayes Classifier,” Sistemasi, vol. 10, no. 1, p. 131, 2021, doi: 10.32520/stmsi.v10i1.1118.

M. Syarifuddin, “Analisis Sentimen Opini Publik Mengenai Covid-19 Pada Twitter Menggunakan Metode Naïve Bayes Dan Knn,” Inti Nusa Mandiri, vol. 15, no. 1, pp. 23–28, 2020.

Yuyun, N. Hidayah, and S. Sahibu, “Algoritma Multinomial Naïve Bayes Untuk Klasifikasi Sentimen Pemerintah Terhadap Penanganan Covid-19 Menggunakan Data Twitter,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 10, pp. 820–826, 2021.

R. Aulianita and A. Rifai, “Optimasi Particle Swarm Optimization Pada Naive Bayes Untuk Sentiment Analysis Furniture,” Inf. Manag. Educ. Prof., vol. 3, no. 1, pp. 31–40, 2018.

A. Faisal, Y. Alkhalifi, A. Rifai, and W. Gata, “Analisis Sentimen Dewan Perwakilan Rakyat Dengan Algoritma Klasifikasi Berbasis Particle Swarm Optimization,” JOINTECS (Journal Inf. Technol. Comput. Sci., vol. 5, no. 2, p. 61, 2020, doi: 10.31328/jointecs.v5i2.1362.

A. E. Augustia, R. Taufan, Y. Alkhalifi, and W. Gata, “Analisis Sentimen Omnibus Law Pada Twitter Dengan Algoritma Klasifikasi Berbasis Particle Swarm Optimization,” Paradigma, vol. 23, no. 2, pp. 158–166, 2021.

M. N. Ardhiansyah, R. Umar, and Sunardi, “Analisis Sentimen pada Twitter Menggunakan Metode Support Vector Machine,” Semin. Nas. Teknol. Fak. Tek. Univ. Krisnadwipayana, vol. 1, no. 1, pp. 739–742, 2019, [Online]. Available: https://jurnal.teknikunkris.ac.id/index.php/semnastek2019/article/view/343/342.

B. W. Sari and F. F. Haranto, “Implementasi Support Vector Machine Untuk Analisis Sentimen Pengguna Twitter Terhadap Pelayanan Telkom Dan Biznet,” J. Pilar Nusa Mandiri, vol. 15, no. 2, pp. 171–176, 2019, doi: 10.33480/pilar.v15i2.699.

Warjiyono, S. Aji, and Fandhilah, “The Sentiment Analysis of Fintech Users Using Support Vector Machine and Particle Swarm Optimization Method,” 7th Int. Conf. Cyber IT Serv. Manag. (CITSM 2019) Fig., 2019.

Q. BAI, “Analysis of Particle Swarm Optimization Algorithm Qinghai,” Comput. Inf. Sci., vol. 3, no. 1, pp. 180–184, 2010, doi: 10.1007/s13369-019-03991-8.

X. Bai, X. Gao, and B. Xue, “Particle Swarm Optimization Based Two-Stage Feature Selection in Text Mining,” 2018 IEEE Congr. Evol. Comput. CEC 2018 - Proc., pp. 1–8, 2018, doi: 10.1109/CEC.2018.8477773.

J. Eka Sembodo, E. Budi Setiawan, and Z. Abdurahman Baizal, “Data Crawling Otomatis pada Twitter,” no. September, pp. 11–16, 2016, doi: 10.21108/indosc.2016.111.

A. Guterres, Gunawan, and J. Santoso, “Stemming Bahasa Tetun Menggunakan Pendekatan Rule Based,” Teknika, vol. 8, no. 2, pp. 142–147, 2019, doi: 10.34148/teknika.v8i2.224.

S. A. Putri, “Prediksi Cacat Software Dengan Teknik Sampel Dan Seleksi Fitur Pada Bayesian Network,” J. Kaji. Ilm., vol. 19, no. 1, p. 17, 2019, doi: 10.31599/jki.v19i1.314.

Styawati and K. Mustofa, “A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification,” vol. 13, no. 3, pp. 219–230, 2019.

R. Wijayanti and A. Arisal, “Ensemble approach for sentiment polarity analysis in user-generated Indonesian text,” Proc. - 2017 Int. Conf. Comput. Control. Informatics its Appl. Emerg. Trends Comput. Sci. Eng. IC3INA 2017, vol. 2018-Janua, pp. 158–163, 2017, doi: 10.1109/IC3INA.2017.8251759.

A. Amolik, N. Jivane, M. Bhandari, and M. Venkatesan, “Twitter sentiment analysis of movie reviews using machine learning technique,” Int. J. Eng. Technol., vol. 7, no. 6, pp. 2038–2044, 2016.

I. Assayyis, I. Cholissodin, and Tibyani, “Optimasi Travelling Salesman Problem Pada Angkutan Sekolah Menggunakan Algoritme Genetika ( Studi Kasus : Sekolah MI Salafiyah Kasim Blitar ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 3, no. 1, pp. 454–461, 2020.

M. H. Rifqo and N. D. M. Veronica, “Implementasi Algoritme Naïve Bayes Berbasis Particle Swarm Optimization Dalam Penentuan Pemberian Kredit,” Pseudocode, vol. 6, no. 1, pp. 1–12, 2019, doi: 10.33369/pseudocode.6.1.1-12.

J. Winahyu and I. Suharjo, “Aplikasi Web Analisis Sentimen Dengan Algoritma Multinomial Naïve Bayes,” Kumpul. Artik. Mhs. Pendidik. Tek. Inform., vol. 10, no. 2, p. 206, 2021, doi: 10.23887/karmapati.v10i2.36609.

T. T. Maskoen and D. Purnama, “Area Under the Curve dan Akurasi Cystatin C untuk Diagnosis Acute Kidney Injury pada Pasien Politrauma,” Maj. Kedokt. Bandung, vol. 50, no. 4, pp. 259–264, 2018, doi: 10.15395/mkb.v50n4.1342.

DOI: https://doi.org/10.32520/stmsi.v11i2.1737

Article Metrics

Abstract view : 467 times
PDF - 227 times

Refbacks

There are currently no refbacks.

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

Username
Password
Remember me