DOI of the published article https://doi.org/10.35145/joisie.v8i2.4764
Analysis of Public Sentiment on Government in Ransomware Attacks Using a Smote Approach
Analisis Sentimen Publik pada Pemerintah dalam Serangan Ransomware dengan Pendekatan Smote
DOI:
https://doi.org/10.21070/ups.6971Keywords:
ransomware, support vector machine, random forest, naïve bayes, SMOTEAbstract
The ransomware attack on Indonesia's national data center has become a widely discussed topic in society. YouTube has become the main platform for disseminating information and giving people opinions. This research aims to identify public sentiment regarding the government's handling of ransomware attacks through analysis of comments on the CNN Indonesia and MetroTV YouTube channels. Data was collected using web scraping techniques and entered into a classification model with three labels positive, neutral and negative. The three machine learning models that will be used are SVM, Random Forest, and Naïve Bayes, with two test scenarios, using Synthetic Minority Over-sampling Technique (SMOTE) and without SMOTE. Applying SMOTE increases model accuracy, in SVM which reaches 96%. The research results show that the comments express negative sentiments towards the government's. This research will provide an understanding of public perceptions of cyber security issues in Indonesia and the effectiveness of SMOTE in sentiment analysis.
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