Analisis Sentimen Kendaraan Listrik Pada Twitter Menggunakan Metode Long Short Term Memory
DOI:
https://doi.org/10.59841/saber.v2i1.857Keywords:
Sentiment Analysis, Electric Vehicles, Twitter, LSTMAbstract
In facing the increasing awareness of environmental impact, electric vehicles have become a primary focus in the global automotive industry. With the advancement of technology and the growing need for eco-friendly solutions, the evaluation of public sentiment towards electric vehicles becomes highly relevant. This research aims to analyze opinions expressed on Twitter regarding the use of electric vehicles using the Long Short Term Memory (LSTM) classification method. Utilizing a dataset of 30,000 entries, this study applies the LSTM algorithm to classify sentiment in tweets. Four different scenarios are tested, involving combinations of Continuous Bag of Words (CBOW) and Skip-Gram feature extraction methods, as well as data split percentages of 80:20 and 70:30. The research results demonstrate high accuracy levels across all scenarios, ranging from 85.16% to 85.9%. These findings indicate the effectiveness of sentiment analysis in gauging public perspectives on the use of electric vehicles. This study makes a significant contribution to understanding public sentiment related to electric vehicles based on Twitter data while highlighting the application of sentiment analysis techniques in the context of electric vehicle usage.
References
Agustian, T., Tukiro, T., & Nurapriani, F. (2022). Analisis Sentimen, Text Mining Penerapan Analisis Sentimen Dan Naive Bayes Terhadap Opini Penggunaan Kendaraan Listrik Di Twitter. TIKA, 7(3), 243-249. doi: https://doi.org/10.51179/tika.v7i3.1550.
Ahmad, A., Khan, Z., Alam, M., & Khateeb, S. (2018). A Review of The Electric Vehicle Charging Techniques, Standards, Progression and Evolution of EV Technologies in German. Smart Science, 6(1), 36-53.
Alhari, M. I., Pratiwi, O. N., & Lubis, M. (2022). Sentiment Analysis of The Public Perspective on Electric Cars in Indonesia Using Support Vector Machine Algorithm. In Proceedings of the 2022 International Conference of Science and Information Technology in Smart Administration (ICSINTESA). DOI: 10.1109/ICSINTESA56431.2022.10041604.
Aslam, N., Rustam, F., Lee, E., Ashraf, A., & Bernard, P. (2022). Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model. IEEE Access. doi: 10.1109/ACCESS.2022.3165621.
Darwis, D., Siskawati, N., & Abidin, Z. (2021). Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Review Data Twitter BMKG Nasional. Jurnal Tekno Kompak, 15(1), 131. doi: 10.33365/jtk.v15i1.744.
Deng, L., & Yu, D. (2012). Deep Learning: Methods and Applications. Foundation and Trends in Signal Process., 7(3–4), 197–387, 2014. doi: 10.1561/2000000039.
Haryadi, D., & Kusuma, G. P. (2019). Emotion Detection in Text using Nested Long Short-Term Memory. International Journal of Advanced Computer Science and Applications (IJACSA), 10(6).
Hochreiter, S., & Schmidhuber, U. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780.
JDIH BPK RI. (n.d.). Percepatan Program Kendaraan Bermotor Listrik Berbasis Baterai (Battery Electric Vehicle) untuk Transportasi Jalan. URL: https://peraturan.bpk.go.id/Home/Details/116973/perpres-no-55-tahun-2019.
Kemp, S. (2023). Digital 2023: Indonesia. https://datareportal.com/reports/digital-2023-indonesia>.
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers.
Liu, B., Hu, M., & Cheng, J. (2005). Opinion Observer: Analyzing and Comparing Opinions on the Web. In Proceedings of the 14th International World Wide Web Conference (WWW-2005), May 10-14, Chiba, Japan.
Nurrohmat, M. A., & Sn, A. (Jul. 2019). Sentiment Analysis of Novel Review Using Long Short-Term Memory Method. IJCCS, 13(3), 209. doi: 10.22146/ijccs.41236.
Olah, C. (2015). Understanding LSTM Networks. URL: https://colah.github.io/posts/2015-08-Understanding-LSTMs/.
Song, Y., Li, J., Ji, G., & Xue, Z. (2016). Study on The Typical Mode EV Charging and Battery Swap Infrstructure Interconnecting to Power Grid. China International Conference on Electric Distribution.
Vrigazova, B. (2021). The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification Problems. Business Systems Research Journal. DOI: https://doi.org/10.2478/bsrj-2021-0015.
Wahid, D. H., & Azhari, S. N. (2016). Peringkasan Sentimen Esktraktif di Twitter Menggunakan Hybrid TF-IDF dan Cosine Similarity. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 10(2), 207-218.
Wilie, B., Vincentio, K., Winata, G. I., Cahyawijaya, S., Li, X., Lim, Z. Y., Soleman, S., Mahendra, R., Fung, P., Bahar, S., & Purwarianti, A. (2020). IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 SABER : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
