CD Tugas Akhir
Analisis Sentimen Masyarakat Terhadap Kepuasan Belajar Dalam Jaringan (Daring) Di Masa New Normal Menggunakan Metode Naïve Bayes Classifier
Online learning is one of the methods of online learning or done over the internet.
Until now in Indonesia began to implement the policy of teaching and learning
activities remotely or online-based learning by utilizing learning applications as a
learning medium is one of the efforts to prevent the widespread transmission of
corona virus (Covid-19) in the world of education. The policy of learning activities
remotely began to be widely discussed not only through the real world but
cyberspace through social media twitter so that it became a trending topic. The
purpose of this study is to find out the percentage number of Pro-Cons in the
community on social media twitter by using the classification method Naïve Bayes
Classifier. The data used is tweets containing #BelajarDaring and
#BelajarDariRumah from September to October 2020. Data collection using
twitterscraper loaded using pyhton. Then pre-processing on data that includes case
folding, tokenizing, normalization, stopword removal, and stemming. The labeling
process results in two classes of data, positive and negative, with a total of 417 data.
The results of this study found that 69% of people's perception is Cons and 31% of
public perception is Pro, against online learning 2020. Data that already has a class
is then divided (Split) data by 70% training data and 30% testing data to be used in
the Naïve Bayes Classifier algorithm. The evaluation showed an accuracy score of
79.63%, a recall value of 88.49% and a precision value of 66.66% based on 417
tweet data consisting of 291 training data and 126 test data.
Keywords : Sentiment Analysis, Confusion Matrix, Naïve Bayes Classifier
Tidak tersedia versi lain