SENTIMENT ANALYSIS MODEL ON ELECTRIC VEHICLES USING INDOBERTWEET AND INDOBERT ALGORITHM
DOI:
https://doi.org/10.35457/w5r3g517Keywords:
analisis sentimen, IndoBERT, IndoBERTweet, kendaraan listrikAbstract
The increasing adoption of electric vehicles in Indonesia has sparked various public opinions, necessitating sentiment analysis to understand societal perspectives. This study aims to compare the performance of two transformer-based models, IndoBERTweet and IndoBERT, in analyzing sentiments towards electric vehicles in Indonesia. Using a dataset collected from Indonesian language tweets and online comments, the data undergoes preprocessing, sentiment labelling into positive, negative, and neutral sentiments, and subsequent fine-tuning of both models. The models are evaluated based on accuracy, precision, recall, and F1-score. Experimental results demonstrate that IndoBERTweet achieves superior performance compared to IndoBERT in sentiment classification. The best performance recorded for IndoBERTweet was an accuracy of 82,40%, with an F1-score of 82,39%, while IndoBERT achieved an accuracy of 75,98% and an F1-score of 75,46%. These findings highlight the importance of using domain-spesific models for sentiment analysis and contribute to advancements in Indonesia-language natural language processing (NLP).
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References
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[2] R. A. Ekatama, M. Rahardi, A. Aminuddin, dan F. F. Abdulloh, “Sentiment Analysis of Electric Vehicles in Indonesia Using Support Vector Machine and Naïve Bayes,” dalam 2023 3rd International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS), IEEE, Des 2023, hlm. 120–125. doi: 10.1109/ICON-SONICS59898.2023.10435277.
[3] A. Erfina dan R. A. Lestari, “Sentiment Analysis of Electric Vehicles using the Naïve Bayes Algorithm,” SISTEMASI, vol. 12, no. 1, hlm. 178, Jan 2023, doi: 10.32520/stmsi.v12i1.2417.
[4] S. Alfarizi dan E. Fitriani, “Analisis Sentimen Kendaraan Listrik Menggunakan Algoritma Naive Bayes dengan Seleksi Fitur Information Gain dan Particle Swarm Optimization,” Indonesian Journal on Software Engineering (IJSE), vol. 9, no. 1, hlm. 19–27, 2023, [Daring]. Tersedia pada: http://ejournal.bsi.ac.id/ejurnal/index.php/ijse
[5] Nurul Afifah, Dony Permana, Dodi Vionanda, dan Dina Fitria, “Sentiment Analysis of Electric Cars Using Naive Bayes Classifier Method,” UNP Journal of Statistics and Data Science, vol. 1, no. 4, hlm. 289–296, Agu 2023, doi: 10.24036/ujsds/vol1-iss4/68.
[6] M. I. Alhari, O. N. Pratiwi, dan M. Lubis, “Sentiment Analysis of The Public Perspective Electric Cars in Indonesia Using Support Vector Machine Algorithm,” dalam 2022 International Conference of Science and Information Technology in Smart Administration (ICSINTESA), IEEE, Nov 2022, hlm. 155–160. doi: 10.1109/ICSINTESA56431.2022.10041604.
[7] M. Daffa Attariq dan R. Jayadi, “ANALYSIS OF INDONESIAN PEOPLE’S SENTIMENT TOWARDS ELECTRIC CARS ON SOCIAL MEDIA,” J Theor Appl Inf Technol, vol. 102, no. 8, 2024, [Daring]. Tersedia pada: www.jatit.org
[8] A. Sri Widagdo, Ardiansyah, Krisna Nuresa Qodri, Fachruddin Edi Nugroho Saputro, dan Nisrina Akbar Rizky Putri, “Analisis Sentimen Mobil Listrik di Indonesia Menggunakan Long-Short Term Memory (LSTM),” JURNAL FASILKOM, vol. 13, no. 3, hlm. 416–423, Des 2023, doi: 10.37859/jf.v13i3.6303.
[9] R. Merdiansah, S. Siska, dan A. Ali Ridha, “Analisis Sentimen Pengguna X Indonesia Terkait Kendaraan Listrik Menggunakan IndoBERT,” Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI), vol. 7, no. 1, hlm. 221–228, Mar 2024, doi: 10.55338/jikomsi.v7i1.2895.
[10] M. N. Hidayat dan R. Pramudita, “Analisis Sentimen Terhadap Pembelajaran Secara Daring Pasca Pandemi Covid-19 Menggunakan Metode IndoBERT,” INFORMATION MANAGEMENT FOR EDUCATORS AND PROFESSIONALS : Journal of Information Management, vol. 8, no. 2, hlm. 161, Jan 2024, doi: 10.51211/imbi.v8i2.2719.
[11] M. N. Hidayat dan R. Pramudita, “Analisis Sentimen Terhadap Pembelajaran Secara Daring Pasca Pandemi Covid-19 Menggunakan Metode IndoBERT,” INFORMATION MANAGEMENT FOR EDUCATORS AND PROFESSIONALS : Journal of Information Management, vol. 8, no. 2, hlm. 161, Jan 2024, doi: 10.51211/imbi.v8i2.2719.
[12] P. Sayarizki dan H. Nurrahmi, “Implementation of IndoBERT for Sentiment Analysis of Indonesian Presidential Candidates,” Journal on Computing, vol. 9, no. 2, hlm. 61–72, 2024, doi: 10.34818/indojc.2024.9.2.934.
[13] N. Nurhasiyah, R. Dwiyansaputra, S. Ika Murpratiwi, dan A. Aranta, “ANALISIS SENTIMEN PENGGUNA PLATFORM MEDIA SOSIAL X PADA TOPIK PEMILIHAN PRESIDEN 2024 MENGGUNAKAN PERBANDINGAN MODEL MONOLINGUAL DAN MULTILINGUAL BERT,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 1, hlm. 626–634, Des 2024, doi: 10.36040/jati.v9i1.12430.
[14] J. C. Setiawan, K. M. Lhaksmana, dan B. Bunyamin, “Sentiment Analysis of Indonesian TikTok Review Using LSTM and IndoBERTweet Algorithm,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 8, no. 3, hlm. 774–780, Agu 2023, doi: 10.29100/jipi.v8i3.3911.
[15] F. Koto, A. Rahimi, J. H. Lau, dan T. Baldwin, “IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP,” Nov 2020, [Daring]. Tersedia pada: http://arxiv.org/abs/2011.00677
[16] J. Howard dan S. Ruder, “Universal Language Model Fine-tuning for Text Classification,” dalam Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Stroudsburg, PA, USA: Association for Computational Linguistics, 2018, hlm. 328–339. doi: 10.18653/v1/P18-1031.
[17] V. Chandradev, I. Made, A. Dwi Suarjaya, I. Putu, dan A. Bayupati, “Chandradev, Analisis Sentimen Review Hotel menggunakan Metode Deep Learning BERT 107 Analisis Sentimen Review Hotel Menggunakan Metode Deep Learning BERT.”
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2025-11-29
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How to Cite
[1]
“SENTIMENT ANALYSIS MODEL ON ELECTRIC VEHICLES USING INDOBERTWEET AND INDOBERT ALGORITHM”, antivirus, vol. 19, no. 2, pp. 192–202, Nov. 2025, doi: 10.35457/w5r3g517.
