Sentiment Analysis on the Construction of the Jakarta-Bandung High-Speed Train on Twitter Social Media Using Recurrent Neural Networks Method

  • Titan Kinan Salaatsa Telkom University
  • Yuliant Sibaroni
Keywords: high-speed train, sentiment analysis, recurrent neural networks, twitter


During the construction of the Jakarta-Bandung high-speed train, many Indonesian people gave their responses to the public. The answers were also varied, with some giving positive and negative reactions. The purpose of this study is to analyze the sentiments of the responses given by the public to the construction of the Jakarta-Bandung high-speed train on Indonesian-language Twitter. To perform sentiment analysis, tweet data was collected utilizing data crawling based on keywords related to the construction of the Jakarta-Bandung high-speed train and given positive, negative, and neutral labels and then represented into numbers using the Keras tokenizer. The method used for sentiment classification of tweet data is the Recurrent Neural Networks method. The highest accuracy results were obtained using the GRU architecture with an accuracy of 69.62%.


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K. Mouratidis, S. Peters, and B. van Wee, “Transportation technologies, sharing economy, and teleactivities: Implications for built environment and travel,” Transp. Res. Part D Transp. Environ., vol. 92, no. February, p. 102716, 2021, doi: 10.1016/j.trd.2021.102716.

M. A. Hairi, “Governance and administrative process of the Light Rail Train project in Palembang, Indonesia,” Public Adm. Policy, vol. 23, no. 3, pp. 299–313, 2020, doi: 10.1108/PAP-06-2020-0031.

T. Tjahjono, A. Kusuma, N. Tinumbia, and A. Septiawan, “The Indonesia high-speed train traveler preference analysis (case study: Jakarta- Bandung),” AIP Conf. Proc., vol. 2227, no. May, 2020, doi: 10.1063/5.0005009.

E. Sutoyo and A. Almaarif, “Twitter sentiment analysis of the relocation of Indonesia’s capital city,” Bull. Electr. Eng. Informatics, vol. 9, no. 4, pp. 1620–1630, 2020, doi: 10.11591/eei.v9i4.2352.

K. M. Carley, M. Malik, M. Kowalchuck, J. Pfeffer, and P. Landwehr, “Twitter Usage in Indonesia,” SSRN Electron. J., 2018, doi: 10.2139/ssrn.2720332.

C. W. Park and D. R. Seo, “Sentiment analysis of Twitter corpus related to artificial intelligence assistants,” 2018 5th Int. Conf. Ind. Eng. Appl. ICIEA 2018, pp. 495–498, 2018, doi: 10.1109/IEA.2018.8387151.

Q. Tul et al., “Sentiment Analysis Using Deep Learning Techniques: A Review,” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 6, 2017, doi: 10.14569/ijacsa.2017.080657.

N. C. Dang, M. N. Moreno-García, and F. De la Prieta, “Sentiment analysis based on deep learning: A comparative study,” Electron., vol. 9, no. 3, 2020, doi: 10.3390/electronics9030483.

Z. Jianqiang, G. Xiaolin, and Z. Xuejun, “Deep Convolution Neural Networks for Twitter Sentiment Analysis,” IEEE Access, vol. 6, pp. 23253–23260, 2018, doi: 10.1109/ACCESS.2017.2776930.

Merinda Lestandy, Abdurrahim Abdurrahim, and Lailis Syafa’ah, “Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 4, pp. 802–808, 2021, doi: 10.29207/resti.v5i4.3308.

R. Ni and H. Cao, “Sentiment Analysis based on GloVe and LSTM-GRU,” Chinese Control Conf. CCC, vol. 2020-July, pp. 7492–7497, 2020, doi: 10.23919/CCC50068.2020.9188578.

S. Yang, X. Yu, and Y. Zhou, “LSTM and GRU Neural Network Performance Comparison Study: Taking Yelp Review Dataset as an Example,” Proc. - 2020 Int. Work. Electron. Commun. Artif. Intell. IWECAI 2020, no. June, pp. 98–101, 2020, doi: 10.1109/IWECAI50956.2020.00027.

M. S. Saputri, R. Mahendra, and M. Adriani, “Emotion Classification on Indonesian Twitter Dataset,” Proc. 2018 Int. Conf. Asian Lang. Process. IALP 2018, no. January 2019, pp. 90–95, 2019, doi: 10.1109/IALP.2018.8629262.

V. Vinayakumar, M. Alazab, A. Jolfaei, S. Kp, and P. Poornachandran, “Ransomware triage using deep learning: Twitter as a case study,” Proc. - 2019 Cybersecurity Cyberforensics Conf. CCC 2019, no. Ccc, pp. 67–73, 2019, doi: 10.1109/CCC.2019.000-7.

N. K. Manaswi, “Deep Learning with Applications Using Python,” Deep Learn. with Appl. Using Python, vol. 1, pp. 115–126, 2018, doi: 10.1007/978-1-4842-3516-4.

Silvin, “Analisis Sentimen Media Twitter Menggunakan Long Short-Term Memory Recurrent Neural Network,” 2019.

S. Shofura, S. Suryani M.Si, L. Salma, and S. Harini, “The Effect of Number of Factors and Data on Monthly Weather Classification Performance Using Artificial Neural Networks,” Int. J. Inf. Commun. Technol., vol. 7, no. 2, pp. 23–35, 2021, doi: 10.21108/ijoict.v7i2.602.

K. Ramasubramanian and A. Singh, “Machine Learning Using R,” Mach. Learn. Using R, pp. 667–688, 2019, doi: 10.1007/978-1-4842-4215-5.

H. M. Lynn, S. B. Pan, and P. Kim, “A Deep Bidirectional GRU Network Model for Biometric Electrocardiogram Classification Based on Recurrent Neural Networks,” IEEE Access, vol. 7, pp. 145395–145405, 2019, doi: 10.1109/ACCESS.2019.2939947.

A. Luque, A. Carrasco, A. Martín, and A. de las Heras, “The impact of class imbalance in classification performance metrics based on the binary confusion matrix,” Pattern Recognit., vol. 91, pp. 216–231, 2019, doi: 10.1016/j.patcog.2019.02.023.

How to Cite
Kinan Salaatsa, T., & Yuliant Sibaroni. (2022). Sentiment Analysis on the Construction of the Jakarta-Bandung High-Speed Train on Twitter Social Media Using Recurrent Neural Networks Method. Digital Zone: Jurnal Teknologi Informasi Dan Komunikasi, 13(2), 102-110.
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