Indonesia Covid-19 Pandemic Social Media Analysis With Text Mining
7th International Conference on Information Management and Technology, ICIMTech 2022
; : 94-99, 2022.
Article
in English
| Scopus | ID: covidwho-2136276
ABSTRACT
This research aimed to analyze public opinion regarding Indonesia's situation in facing community restrictions (PPKM) during the Covid-19 pandemic. This research used the text mining approach to classify public opinion into two classes of the Pro and Cons classes regarding the policies, along with comparing the accuracy, precision, and recall values using two text classification methods of Naive Bayes and Support Vector Machine (SVM). The data collected were 217 tweets from Indonesia in November 2020. The Naive Bayes method showed 64% accuracy, 72% precision, and 53% recall, while the SVM method showed 63% accuracy, 70% precision, and 53 % recall. Based on these classification text methods results, researchers concluded that SVM's accuracy, precision, and recall values were not higher than Naive Bayes. © 2022 IEEE.
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
7th International Conference on Information Management and Technology, ICIMTech 2022
Year:
2022
Document Type:
Article
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