Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Pada Analisis Sentimen Kebijakan Kemdikbudristek Mengenai Kuota Internet Selama Covid-19


  • Ulfa Khaira Universitas Jambi
  • Reni Aryani Universitas Jambi
  • Reza Wahyu Hardian Universitas Jambi




Sentiment analysis, Naïve Bayes, Support Vector Machine (SVM)


Sentiment analysis is an activity that is used to analyze public opinion about an incident such as the Ministry of Education and Culture's internet assistance quota during the Covid-19 pandemic through one of the Twitter social media. Twitter is a microblogging platform that is used to write an opinion or opinion about an event that can be used as a source of data used. The Naïve Bayes method and Support Vector Machine (SVM) are methods with a Machine Learning approach that can be used to perform sentiment analysis on Kemdikbudristek policies regarding MoEC Quotas in the process of classifying a tweet based on its emotional level and knowing the accuracy comparison between the Naïve Bayes method and the Support Vector Machine ( SVM). The results of the sentiment analysis process using the Naïve Bayes Algorithm and Support Vector Machine (SVM) based on public opinion, in this case Twitter users regarding the Ministry of Education and Culture Quota policies, resulted in a higher level of accuracy for the Support Vector Machine (SVM) than Naïve Bayes with an accuracy of 80%, for an average -the average precision value is 80.3%, recall is 80.3% and f1-score is 80.3%.


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Khaira, U., Aryani, R., & Hardian, R. W. (2023). Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Pada Analisis Sentimen Kebijakan Kemdikbudristek Mengenai Kuota Internet Selama Covid-19. Jurnal PROCESSOR, 18(2). https://doi.org/10.33998/processor.2023.18.2.897