KLASIFIKASI SENTIMEN TERHADAP DATA TEXT JEJARING SOSIAL DENGAN TOPIK VAKSIN COVID-19 MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM)

SOLEHAN, ILHAM and Ubaya, Huda (2021) KLASIFIKASI SENTIMEN TERHADAP DATA TEXT JEJARING SOSIAL DENGAN TOPIK VAKSIN COVID-19 MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM). Undergraduate thesis, Sriwijaya University.

	Text RAMA_56201_09011281621034.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_56201_09011281621034_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (7MB) | Request a copy
Preview	Text RAMA_56201_09011281621034_0216068101_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (1MB) | Preview
	Text RAMA_56201_09011281621034_0216068101_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (471kB) | Request a copy
	Text RAMA_56201_09011281621034_0216068101_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_56201_09011281621034_0216068101_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (986kB) | Request a copy
	Text RAMA_56201_09011281621034_0216068101_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (89kB) | Request a copy
	Text RAMA_56201_09011281621034_0216068101_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (167kB) | Request a copy
	Text RAMA_56201_09011281621034_0216068101_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (625kB) | Request a copy

Abstract

In August 2020, the government announced it had found a coronavirus vaccine and distributed it to the public to stop the pandemic. This time the government's policy triggered many public reactions in the form of negative and positive sentiments about the vaccine. The greater number of sentiments circulating in the community regarding the ongoing pandemic and the high public enthusiasm for the government's Covid-19 Vaccine policy product this time, the classification of public sentiment towards this vaccine is deemed necessary. Community sentiments are recorded and collected from a social network, namely the Twitter social network where there are many public sentiments on the topic of the Covid-19 Vaccine. The classification of these sentiments will use Natural Language Processing (NLP) & Support Vector Machine (SVM) as the classification method. From the experiments conducted with 80% data split of training data with 20% of test data resulted in the highest Precision, Recall and F1-Score values, while for experiments with 20% data split of training data with 80% of test data yielded Precision, Recall and The lowest F1-Score. Data classification can be carried out properly using the Support Vector Machine method, so that it can create a system that is able to classify sentiments on the topic of the Covid-19 Vaccine. The results of the prediction of sentiment text data using the Support Vector Machine method can be used as consideration or reference for government decision making in making policies regarding the Covid-19 vaccine topic.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Natural Language Processing, Support Vector Machine, Vaksin Covid-19, sentimen, data text.
Subjects:	H Social Sciences > HN Social history and conditions. Social problems. Social reform > HN1-995 Social history and conditions. Social problems. Social reform
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Mr. Ilham Solehan
Date Deposited:	02 Sep 2021 04:04
Last Modified:	02 Sep 2021 04:04
URI:	http://repository.unsri.ac.id/id/eprint/53257

Actions (login required)

View Item