SENTIMENT ANALYSIS OF ELECTRIC CARS USING RECURRENT NEURAL NETWORK METHOD IN INDONESIAN TWEETS

  • Felisia Handayani Department of Computer Science and Information Technology Gunadarma University
  • Metty Mustikasari Department of Computer Science and Information Technology Gunadarma University

Abstract

Sentiment analysis is computational research of the opinions of many people who are textually expressed against a particular topic. Twitter is the most popular communication tool among Internet users today to express their opinions. Deep Learning is a solution to allow computers to learn from experience and understand the world in terms of the hierarchy concept. Deep Learning objectives replace manual assignments with learning. The development of deep learning has a set of algorithms that focus on learning data representation. The recurrent Neural Network is one of the machine learning methods included in Deep learning because the data is processed through multi-players. RNN is also an algorithm that can recall the input with internal memory, therefore it is suitable for machine learning problems involving sequential data. The study aims to test models that have been created from tweets that are positive, negative, and neutral sentiment to determine the accuracy of the models. The models have been created using the Recurrent Neural Network when applied to tweet classifications to mark the individual classes of Indonesian-language tweet data sentiment. From the experiments conducted, results on the built system showed that the best test results in the tweet data with the RNN method using Confusion Matrix are with Precision 0.618, Recall 0.507 and Accuracy 0.722 on the data amounted to 3000 data and comparative data training and data testing of ratio data 80:20

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Published
2020-12-07
How to Cite
HANDAYANI, Felisia; MUSTIKASARI, Metty. SENTIMENT ANALYSIS OF ELECTRIC CARS USING RECURRENT NEURAL NETWORK METHOD IN INDONESIAN TWEETS. Jurnal Ilmiah Kursor, [S.l.], v. 10, n. 4, dec. 2020. ISSN 2301-6914. Available at: <http://kursorjournal.org/index.php/kursor/article/view/233>. Date accessed: 18 may 2021. doi: https://doi.org/10.21107/kursor.v10i4.233.
Section
Articles