Analisis Sentimen Masyarakat Indonesia Mengenai Vaksin COVID-19 Pada Media Sosial Twitter Menggunakan Metode Naïve Bayes Classifier dan Support Vector Machine

Permatasari, Rizka Widya (2021) Analisis Sentimen Masyarakat Indonesia Mengenai Vaksin COVID-19 Pada Media Sosial Twitter Menggunakan Metode Naïve Bayes Classifier dan Support Vector Machine. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

	Text 06211940005004-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 October 2023. Download (1MB) | Request a copy
	Text 06211940005004-Undergraduate_Thesis.pdf Restricted to Repository staff only Download (1MB) | Request a copy

Abstract

World Health Organization (WHO) mendeklarasi-kan virus COVID-19 sebagai pandemi global pada 11 Maret 2020. Kondisi tersebut memberikan dampak langsung kepada seluruh masyarakat di dunia, dengan mulai diberlakukannya protokol ke-sehatan yang harus diterapkan pada seluruh aspek kegiatan, mulai dari pembatasan sosial hingga lockdown total yang menghambat seluruh kegiatan masyarakat. Salah satu cara yang dilakukan untuk mencegah penyebaran virus ini adalah dengan pemberian vaksin. Kegiatan vaksinasi mulai diberikan kepada masyarakat Indonesia pada bulan Januari 2021. Pada media sosial twitter, pro kontra vaksin COVID-19 sempat menjadi trending topic sehingga dirasa perlu untuk dilakukan penelitian tentang sentimen publik terhadap adanya kegiatan vaksinasi dalam memutus rantai penyebaran COVID-19 di Indonesia. Pada penelitian ini digunakan analisis klasifikasi teks yaitu Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM). NBC telah banyak digunakan dalam penelitian mengenai Text Mining karena memiliki algoritma yang sederhana namun dapat menghasilkan akurasi yang tinggi, sedangkan SVM memiliki kemampuan yang baik dalam mengolah data berdimensi besar dengan hasil yang efektif. Perbandingan kedua metode menggunakan 10 fold-stratified cross validation dengan kriteria kebaikan klasifikasi AUC dan akurasi menunjukkan bahwa SVM memiliki kinerja klasifikasi yang lebih baik dibanding NBC dan SVM kernel menghasilkan ketepatan klasifikasi lebih tinggi dibanding SVM kernel RBF.
====================================================================================================
The World Health Organization (WHO) declared the COVID-19 virus as a global pandemic on March 11, 2020. These conditions had a direct impact on all people in the world, with the introduction of health protocols that must be applied to all aspects of activities, starting from from social restrictions to total lockdowns that hinder all community activities. One way to prevent the spread of this virus is by giving vaccines. Vaccination activities began to be given to the people of Indonesia in January 2021. On social media Twitter, the pros and cons of the COVID-19 vaccine had become a trending topic, so it was deemed necessary to conduct research on public sentiment towards vaccination activities in breaking the chain of spread of COVID-19. 19 in Indonesia. This research uses text classification analysis, namely Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM). NBC has been widely used in research on Text Mining because it has a simple algorithm but can produce high accuracy, while SVM has a good ability to process large-dimensional data with effective results. Comparison of the two methods using 10 fold-stratified cross validation with AUC classification goodness criteria and accuracy shows that SVM has better classification performance than NBC and SVM kernel produces higher classification accuracy than SVM kernel RBF.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	AUC, COVID-19, Imbalanced, Naïve Bayes Classifier, Stratified Cross Validation. Support Vector Machine, Twitter, Vaksin, AUC, COVID-19, Imbalanced, Naïve Bayes Classifier, Stratified Cross Validation, Support Vector Machine, Twitter, Vaksin
Subjects:	Q Science > Q Science (General) > Q180.55.M38 Mathematical models Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. Q Science > QA Mathematics > QA76.9.D343 Data mining. Querying (Computer science) Q Science > QA Mathematics > QA76.F56 Data structures (Computer science) T Technology > T Technology (General) > T57.8 Nonlinear programming. Support vector machine. Wavelets. Hidden Markov models.
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Rizka widya permatasari
Date Deposited:	01 Sep 2021 04:43
Last Modified:	01 Sep 2021 04:43
URI:	http://repository.its.ac.id/id/eprint/91280

Actions (login required)

View Item