Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination


Rahmaddeni Rahmaddeni(1*); M. Khairul Anam(2); Yuda Irawan(3); Susanti Susanti(4); Muhammad Jamaris(5);

(1) STMIK Amik Riau
(2) STMIK Amik Riau
(3) STMIK Hangtuah Pekanbaru
(4) STMIK Amik Riau
(5) STMIK Amik Riau
(*) Corresponding Author

  

Abstract


The corona virus has become a global pandemic and has spread almost all over the world, including Indonesia. There are many negative impacts caused by the spread of COVID-19 in Indonesia, so the government takes vaccination measures in order to suppress the spread of COVID-19. The public's response to vaccination was quite diverse on Twitter, some were supportive and some were not. The data used in this study came from Twitter which was taken using the drone emprit portal, using the keyword, namely "vaccination". The classification will be carried out using the SVM and hybrid methods between SVM and XGBoost or what is commonly called XGBSVM. The purpose of this study is to provide an overview to the public whether the Covid-19 vaccination actions carried out tend to be positive, neutral or negative opinions. The results of the sentiment evaluation that have been carried out can be seen that SVM has the highest accuracy of 83% with 90:10 data splitting, then XGBSVM produces 79% accuracy with 90:10 data splitting.

Keywords


sentiment analysis; vaccination; covid-19; SVM; XGBSVM

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 626 times
PDF view: 200 times 		     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v14i1.1090.32-38 		  

Cite

How to cite item

References


B. Brahimi, M. Touahria, and A. Tari, Improving sentiment analysis in Arabic: A combined approach, J. King Saud Univ. - Comput. Inf. Sci., vol. 33, no. 10, pp. 12421250, 2019, doi: 10.1016/j.jksuci.2019.07.011.

J. Qiu, Z. Lin, and Q. Shuai, Investigating the opinions distribution in the controversy on social media, Inf. Sci. (Ny)., vol. 489, pp. 274288, 2019, doi: 10.1016/j.ins.2019.03.041.

M. K. Anam, B. N. Pikir, M. B. Firdaus, S. Erlinda, and Agustin, Penerapan Nave Bayes Classifier , K-Nearest Neighbor dan Decision Tree untuk Menganalisis Sentimen pada Interaksi Netizen dan Pemeritah Applications of Nave Bayes Classifier , K-Nearest Neighbor and Decision Tree to Analyze Sentiment on Netizen and Gove, Matrik J. Manajemen, Tek. Inform. dan Rekayasa Komput. ?141, vol. 21, no. 1, pp. 139150, 2021, doi: 10.30812/matrik.v21i1.1092.

P. Arsi and R. Waluyo, Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM), J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, p. 147, 2021, doi: 10.25126/jtiik.0813944.

I. Rozi, S. Pramono, and E. Dahlan, Implementasi Opinion Mining (Analisis Sentimen) Untuk Ekstraksi Data Opini Publik Pada Perguruan Tinggi, J. EECCIS, vol. 6, no. 1, pp. 3743, 2012.

L. Ardiani, H. Sujaini, and T. Tursina, Implementasi Sentiment Analysis Tanggapan Masyarakat Terhadap Pembangunan di Kota Pontianak, J. Sist. dan Teknol. Inf., vol. 8, no. 2, pp. 4451, 2020, doi: 10.26418/justin.v8i2.36776.

N. T. Romadloni, I. Santoso, and S. Budilaksono, Perbandingan Metode Naive Bayes , Knn Dan Decision Tree Terhadap Analisis Sentimen Transportasi Krl, J. IKRA-ITH Inform., vol. 3, no. 2, pp. 19, 2019.

M. Nurmalasari, N. A. Temesvari, and S. N. Maulana, Analisis Sentimen terhadap Opini Masyarakat dalam Penggunaan Mobile-JKN untuk Pelayanan BPJS Kesehatan Tahun 2019, Indones. Heal. Inf. Manag. J., vol. 8, no. 1, pp. 3544, 2020, [Online]. Available: https://inohim.esaunggul.ac.id/index.php/INO/article/view/208.

C. Juditha, Fenomena Trending Topic Di Twitter: Analisis Wacana Twit #Savehajilulung, J. Penelit. Komun. dan Pembang., vol. 16, no. 2, pp. 138154, 2015, doi: 10.31346/jpkp.v16i2.1353.

R. N. Rahayu and Sensusiyati, Vaksin covid 19 di indonesia : analisis berita hoax, Intelektiva J. Ekon. Sos. Hum. Vaksin, vol. 2, no. 07, pp. 3949, 2021.

F. F. Rachman and S. Pramana, Analisis Sentimen Pro dan Kontra Masyarakat Indonesia tentang Vaksin COVID-19 pada Media Sosial Twitter, Heal. Inf. Manag. J., vol. 8, no. 2, pp. 100109, 2020, [Online]. Available: https://inohim.esaunggul.ac.id/index.php/INO/article/view/223/175.

M. K. Nasution, R. R. Saedudin, and V. P. Widartha, PERBANDINGAN AKURASI ALGORITMA NAVE BAYES DAN ALGORITMA, in e-Proceeding of Engineering, 2021, vol. 8, no. 5, pp. 97659772.

M. F. Al-shufi and A. Erfina, Sentimen Analisis Mengenai Aplikasi Streaming Film Menggunakan Algoritma Support Vector Machine Di Play Store, in SISMATIK, 2021, pp. 156162.

R. Siringoringo, R. Perangin-angin, and M. J. Purba, Segmentasi Dan Peramalan Pasar Retail Menggunakan Xgboost Dan Principal Component Analysis, METHOMIKA J. Manaj. Inform. dan Komputerisasi Akunt., vol. 5, no. 1, pp. 4247, 2021, doi: 10.46880/jmika.vol5no1.pp42-47.

W. Chang, Y. Liu, X. Wu, Y. Xiao, S. Zhou, and W. Cao, A New Hybrid XGBSVM Model: Application for Hypertensive Heart Disease, IEEE Access, vol. 7, pp. 175248175258, 2019, doi: 10.1109/ACCESS.2019.2957367.

F. S. Jumeilah, Penerapan Support Vector Machine (SVM) untuk Pengkategorian Penelitian, J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 1, pp. 1925, 2017, doi: 10.29207/resti.v1i1.11.

A. N. Ulfah and M. K. Anam, Analisis Sentimen Hate Speech Pada Portal Berita Online Menggunakan Support Vector Machine (SVM), JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 7, no. 1, pp. 110, 2020, doi: 10.35957/jatisi.v7i1.196.

S. Khairunnisa, A. Adiwijaya, and S. Al Faraby, Pengaruh Text Preprocessing terhadap Analisis Sentimen Komentar Masyarakat pada Media Sosial Twitter (Studi Kasus Pandemi COVID-19), in Jurnal Media Informatika Budidarma, 2021, vol. 5, no. 2, p. 406, doi: 10.30865/mib.v5i2.2835.

E. S. Romaito, M. K. Anam, Rahmaddeni, and A. N. Ulfah, Perbandingan Algoritma SVM Dan NBC Dalam Analisa Sentimen Pilkada Pada Twitter, CSRID J., vol. 13, no. 3, pp. 169179, 2021, doi: 10.22303/csrid.13.3.2021.169-179.

P. M. Prihatini, Implementasi Ekstraksi Fitur Pada Pengolahan Dokumen Berbahasa Indonesia, J. Matrix, vol. 6, no. 3, pp. 174178, 2016.

R. Melita, V. Amrizal, H. B. Suseno, and T. Dirjam, Penerapan Metode Term Frequency Inverse Document Frequency (Tf-Idf) Dan Cosine Similarity Pada Sistem Temu Kembali Informasi Untuk Mengetahui Syarah Hadits Berbasis Web (Studi Kasus: Hadits Shahih Bukhari-Muslim), J. Tek. Inform., vol. 11, no. 2, pp. 149164, 2018, doi: 10.15408/jti.v11i2.8623.

T. B. Sasongko, Komparasi dan Analisis Kinerja Model Algoritma SVM dan PSO-SVM (Studi Kasus Klasifikasi Jalur Minat SMA), J. Tek. Inform. dan Sist. Inf., vol. 2, no. 2, pp. 244253, 2016, doi: 10.28932/jutisi.v2i2.476.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Rahmaddeni Rahmaddeni, M. Khairul Anam, Yuda Irawan, Susanti Susanti, Muhammad Jamaris

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