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


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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[1] Utami, Lilyani Asri, 2016, analysis of the public opinion sentiment news of forest fires through comparison algorithm Support Vector Machine and K-Nearet Neighbor based Particle swarm Optimization.
[2] Andri, Delvi. 2017. The relationship between the level of Interpersonal trust and the adolescent self-disclosure of Online social Media users at the 2 Holy State High School.
[3] Liu, Bing. 2012. Sentiment Analysis and Opinion Mining. Morgan & Claypool Publisher
[4] Pengfei. Liu. Qiu, Xipeng. Huang, Xianjing, 2016, Recurrent Neural Network For Text Classiļ¬cation With Multi-Task Learning
[5] Brett Duncan And Yanqing Zhang, 2015, Neural Networks For Sentiment Analysis On Twitter.
[6] Ye Yuan, You Zhou, 2015, Twitter Sentiment Analysis With Recursive Neural Networks
[7] Nurrohmat, Muh Amin & Sn, Azhari, 2019, Sentiment Analysis of Novel Review Using Long Short Term Memory Method
[8] Lowe, Ryan Et All, 2017 Training End-To-End Dialogue Systems With The Ubuntu Dialogue Corpus
[9] Kao, Anne. Poteet, Steve R, 2007. Natural Language Processing And Text Mining. Springer International Publishing Science & Business Media
[10] Goodfellow, Ian. Bengio, Yoshua. Courville, Aaron , 2016 , Deep Learning, Mit Press
[11] Suyanto. Michael. Mandala, Satria. 2019, Deep Learning modernization Machine Learning for Big Data. Informatics Publisher
[12] Hemanth, Jude. 2018. Computational Vision and Bio Inspired Computing Springer, Technology & Engineering
[14] Ashish Kumar, Luhach, Dharm Singh, Pao-Ann Hsiung, Kamarul Bin Ghazali Hawari, Pawan Lingras, Pradeep Kumar Singh, 2018. Advanced Informatics For Computing Research: Second International Conference, Icaicr 2018, Shimla, India, July.
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: <>. Date accessed: 18 may 2021. doi: