Analisis Sentimen Reaction Menteri Pendidikan Kabinet Kerja Jilid 2 Pada Twitter Menggunakan Naïve Bayes Dan Multilayer Perception

Sri Wahyu, Riskianti (2020) Analisis Sentimen Reaction Menteri Pendidikan Kabinet Kerja Jilid 2 Pada Twitter Menggunakan Naïve Bayes Dan Multilayer Perception. Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

President Joko Widodo has appointed new ministers who are members of the Working Cabinet Volume 2, one of which is the Minister of Education. With the election of the new Minister of Education, there have been many responses from the public on social media, especially Twitter, both positive and negative responses. So that Twitter is very suitable to be used as a source of sentiment analysis because of the use of Twitter to express their opinions on various topics. To conduct a sentiment analysis, we need a method to classify data into positive and negative classes. The algorithms used in this study are Naïve Bayes and Multilayer Perceptron to find the best and optimal performance method in the sentiment analysis reaction case of the minister of education in the cabinet of work volume 2 on Twitter. Data retrieval is done using the Tweepy API service. The final result of this study is to compare the results of the accuracy of the two algorithms used, and after normalizing the data to produce accuracy, the results show that Naïve Bayes has an accuracy of 71% while the Multilayer Perceptron has an accuracy of 63%. In this case the Naïve Bayes algorithm has better performance than the Multilayer Perceptron. Keywords - Accuracy, API tweepy, Multilayer Perceptron, Naïve Bayes, twitter

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