@thesis{thesis,
		author={Adjie  Firman Anshari},
		title ={SENTIMENT ANALYSIS TERKAIT DAMPAK RUU OMNIBUS LAW CIPTA KERJA MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER},
		year={2021},
		url={http://repository.universitasbumigora.ac.id/1015/},
		abstract={In this period of time, many social media make it easy for us 
to know events and information around the world, some social 
media including Facebook, Twitter, Instagram, social media, 
etc. Twitter is one of the social media that has many users at 
this time. Twitter, apart from being a medium of information 
with various kinds of tweets, Twitter is also often used to 
socialize between users and express their opinions to a 
discussion or issues that are currently happening. The public 
can get information and various comments from Twitter 
because the number is very large, so it can be used for 
sentiment analysis. With so much data, the purpose of this study 
is to create a public sentiment analysis system in the form of 
positive sentiment, negative, and neutral towards the tweet of 
the Omnibus Law on Job Creation. Sentiment Analysis is one 
of the areas found in Machine Learning. Sentiment Analysis 
can be done by using a classification method. One of the 
methods used in sentiment classification is the Naïve Bayes 
Classifier using TF – IDF (Term Frequency – Inverse 
Document Frequency) weighting. This research begins by 
taking twitter data of 5000 tweets, using two methods, namely 
RapidMiner and the Twitter API (Application Programming 
Interface) that has been provided. The data format used in this 
study is CSV (Comma Separated Values). This sentiment will 
later be used as training data and testing data. Then the 
sentiment will be done Preprocessing process in which there 
are several stages. The output results obtained are in the form 
of a system that is able to classify new data by providing 
positive sentiment, neutral, and negative on tweets. Sentiment 
classification experiment using the Naïve Bayes Classifier 
method using the Split Train test and K-fold Cross Validation 
produces a Confusion Matrix with an accuracy of 77.22%. and 
with K-fold Cross Validation of 79.33% , 79% and , 78.7%.
Then the percentage of sentiment results obtained in a row is 
47.3% for neutral sentiment, 35.4% for positive sentiment, and 
17.3% for negative sentimen}
		}