Online School Sentiment Analysis in Indonesia on Twitter Using The Naïve Bayes Classifier and Rapid Miner Tools


Authors : Ahmad Cahyono Adi; Dyan Puji Lestari; Elsa; Fiqrudina Sain Saputri; Yohanes Sabui

Volume/Issue : Volume 7 - 2022, Issue 1 - January

Google Scholar : http://bitly.ws/gu88

Scribd : https://bit.ly/3gOJXmp

DOI : https://doi.org/10.5281/zenodo.6090873

The COVID-19 pandemic entered at the beginning of 2020 which hit various countries in the world, including Indonesia, with 4,259,644 contaminated cases (Kawal Covid 19. 2021). The impact of the COVID19 pandemic is in the economic, tourism, and education sectors. The most obvious impact due to this pandemic is in the field of education, where every process of teaching and learning activities is limited or even encouraged to study from home. Therefore, teaching and learning activities are carried out online or in a network (online). Educators are starting to look for alternative methods used in online learning because in Indonesia they still use conventional learning or are still in the form of face-toface learning directly with a classical system.The Naive Bayes method is a classification using probability and statistical methods, namely by predicting future opportunities based on previous experience. The main feature of the Naïve Bayes classification is to get a strong hypothesis from each condition or event. The following is the equation of Bayes' theorem In the rapid miner tools, the data is then retrieved by taking the text and label attributes that have been given during the labeling process. The data is added with a label attribute to facilitate the classification process. After that, the labeled data is then normalized by changing or removing unimportant attributes, in this case, the unimportant data is other than letters. Therefore, a process of deleting nonletter-type data is needed which includes numbers and symbols.

Keywords : Covid-19; Naïve Bayes Algorithm;Sentiment Analysis; Text Mining;Twitter.

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