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Astri Pertiwi
Agung Triayudi
Endah Tri Esti Handayani

Abstract

Social media platforms and micro blogging websites are rich sources of user-generated data. The internet has changed the way people express their thoughts and feelings through social media, like Twitter. Twitter as a place for information flow, Twitter is a rich source for learning about people's opinions and sentiment analysis. This study aims to determine how much the impact of the co-19 pandemic on economic conditions in Indonesia, measured from the resulting community sentiment. To solve the problem in this case study using a method that is for pre-processing data using cleansing, filtering, tokenization and stemming. The classification process uses the Artificial Neural Networks (ANN) method. The data used are Indonesian-language tweets with the keywords Covid-19, Economi Indonesia, MSME, storylineverline and phk, with a dataset of 1600 tweets. The results of this study are sentiment analysis of the impact of co-19 on the Indonesian economy. Results of training dataSMEs get an accuracy of 99.37% and Ojekonline data get an accuracy of 99.25%.

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How to Cite
Pertiwi, A., Triayudi, A. and Handayani, E. T. E. (2020) “Sentiment Analysis of the Impact of Covid-19 on Indonesia’s Economy through Social Media Using the ANN Method: Sentiment Analysis of the Impact of Covid-19 on Indonesia’s Economy through Social Media Using the ANN Method”, Jurnal Mantik, 4(1), pp. 605-612. Available at: https://iocscience.org/ejournal/index.php/mantik/article/view/789 (Accessed: 24June2021).
References
[1] M. Ahmad, S. Aftab, SS Muhammad, and S. Ahmad, 'Machine Learning Techniques for Sentiment Analysis: A Review ', International Journal of Multidiciplinary Sciences and Engineering, Vol.8, No.3, April (2017), [ISSN: 2045-7057].
[2] S. Bharathi and A. Geetha, 'Sentiment Analysis for Effective Stock Market Prediction ', International Journal of Intelligent Engineering & Systems, ijies2017', Vol. 10, No.3, (2017)
[3] C. Kaur and A. Sharma, 'Twitter Sentiment Analysis on Coronavirus using Textblob ', EasyChair Preprint, No.2974, March 16 (2020)
[4] MO Pratama, W.Satyawan, R.Jannati, B.Pamungkas, Raspiani, ME Syahputra and I. Neforawati, 'The sentiment analysis of Indonesia commuter line using machine learning based on twitter data', 2018 International Conference of Computer and Informatics Engineering (IC2IE), IOP Conf. Series: Journal of Physics:Conf. Series 1193 (2019) 012029
[5] GA Buntoro, 'DKI Jakarta Governor Candidate Sentiment Analysis 2017 on Twitter', Integer Journal, Vol. 2, No. 1, March (2017): 32-41
[6] WA Luqyana, I. Cholissodin, and Prime Hospital, 'Analysis of Cyberbullying Sentiments on Instagram Comments by the Support Vector Machine Classification Method', Journal of Information Technology Development and Computer Science, Vol. 2, No. 11, p. 4707-4713 November (2018)
[7] A. Triayudi, 'Convolutional neural networks for text classifications on Twitter', Journal of Software Engineering & Intelligent Systems,Vol. 4, 31 December (2019), 2016-2019, ISSN: 2518-8739
[8] WC Frans Mariel, S. Mariyah, and A. Permana, 'Sentiment analysis: a comparison of deep learning neural network algorithms with SVM and nave Bayes for Indonesian text ', International Conference on Data and Information Science', IOP Conf. Series: Journal of Physics: Conf.Series 971(2018)
[9] A. Futuhul Hadi, D. Bagus C. W, and M. Hasan, 'Text Mining on Twitter Social Media Case Study: Quiet Period 2017 Round 2 of DKI Jakarta Election, National Seminar on Mathematics and Its Applications, October 21 (2017)
[10] H. Akbar, 'Sentiment Analysis Review of Higher Education Institutions From Facebook Using Artificial Neural Networks', JIK: Journal of Computer Science, Vol.4, No.1, June (2019).
[11] H. Jelodar, Y. Wang, and R. Orji, 'Deep Sentiment Classification and Topic Discovery on Coronavirus Novels or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Networks Approach', bioRxiv preprint, April 24 (2020) .
[12] S. Paliwal, S. Kumar Khatri, and M. Sharma 'Sentiment Analysis and Prediction using Neural Networks, Proceedings