Indonesian Public Sentiment Toward Electric Vehicles: Analysis of Social Media Data

Authors

  • Ni Wayan Sumartini Saraswati Institut Bisnis dan Teknologi Indonesia, Indonesia
  • I Wayan Dharma Suryawan Institut Bisnis dan Teknologi Indonesia, Indonesia
  • I Dewa Made Krishna Muku Institut Bisnis dan Teknologi Indonesia, Indonesia
  • I Kadek Agus Bisena Institut Bisnis dan Teknologi Indonesia, Indonesia
  • Dewa Ayu Kadek Pramita Institut Bisnis dan Teknologi Indonesia, Indonesia

DOI:

10.33395/sinkron.v9i3.15179

Keywords:

EVs, Indonesia, Sentiment Analysis, Support Vector Machine, Social Media X

Abstract

The development of electric vehicles (EVs) in Indonesia has progressed significantly, supported by government subsidies for Battery-Based Electric Motor Vehicles. These subsidies have sparked mixed public reactions that some support them due to environmental benefits and pollution reduction, while others oppose them for various reasons. Social media platform X serves as a valuable source for gauging public opinion, though analyzing such data manually can be complex. To address this, sentiment analysis, particularly using the Support Vector Machine (SVM) method, offers an efficient solution. This study analyzes 23,031 Indonesian-language tweets from social media platform X, collected between October 2023 and July 2024, using SVM for sentiment classification. The best-performing model, with parameter C = 0.5 and without stemming, achieved an accuracy of 84.98%. The findings suggest that Indonesians generally view electric vehicles positively, with more favorable sentiments than negative ones. This study offers implications across methodological, industrial, and policy domains. Word cloud analysis further supports this, highlighting public support in areas such as pricing, infrastructure, and environmental impact. However, the study also identifies key concerns, including issues around subsidies, taxes, vehicle durability, battery types, and import regulations. Overall, the research provides meaningful insights into the diverse perspectives of Indonesian citizens regarding EVs, helping to inform future policy and development strategies.

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References

Alomari, D., & Ahmad, I. (2024). Exploring Character Trigrams for Robust Arabic Text Classification: A Comparative Analysis in the Face of Vocabulary Expansion and Misspelled Words. IEEE Access, 12(March), 57103–57116. https://doi.org/10.1109/ACCESS.2024.3390048

Chamira, S. (2022). Implementasi Metode Text Mining Frequency-Invers Document Frequency (Tf-Idf) Untuk Monitoring Diskusi Online. Journal of Informatics, Electrical and Electronics Engineering, 1(3), 97–102. https://doi.org/10.47065/jieee.v1i3.353

Dinata, R. K., Safwandi, S., Hasdyna, N., & Mahendra, R. (2020). Kombinasi Algoritma Brute Force dan Stemming pada Sistem Pencarian Mashdar. CESS (Journal of Computer Engineering, System and Science), 5(2), 273. https://doi.org/10.24114/cess.v5i2.17989

Ependi, U., Aliya, S., & Wibowo, A. (2023). Sentiment Analysis of Covid-19 Handling in Indonesia Based on Lexicon Weighting. Jurnal Sisfokom (Sistem Informasi Dan Komputer), 12(1), 76–82. https://doi.org/10.32736/sisfokom.v12i1.1615

Fahlevvi, M. R. (2022). Analisis Sentimen Terhadap Ulasan Aplikasi Pejabat Pengelola Informasi Dan Dokumentasi Kementerian Dalam Negeri Republik Indonesia Di Google Playstore Menggunakan Metode Support Vector Machine. Jurnal Teknologi Dan Komunikasi Pemerintahan, 4(1), 1–13. https://doi.org/10.33701/jtkp.v4i1.2701

Faisal, H., Febriandirza, A., & Hasan, F. N. (2024). Analisis Sentimen Terkait Ulasan Pada Aplikasi PLN Mobile Menggunakan Metode Support Vector Machine. KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen), 5(1), 303–312.

GAIKINDO: Permintaan Mobil Listrik Ada, tapi tak Besar. (2023). Retrieved February 28, 2025, from GAIKINDO website: https://www.gaikindo.or.id/gaikindo-permintaan-mobil-listrik-ada-tapi-tak-besar/

Habbat, N., Anoun, H., & Hassouni, L. (2021). A Novel Hybrid Network for Arabic Sentiment Analysis using fine-tuned AraBERT model. International Journal on Electrical Engineering and Informatics, 13(4), 801–812. https://doi.org/10.15676/ijeei.2021.13.4.3

Helmi Satria. (2022). Tweet Harvest. Retrieved from Github website: https://github.com/helmisatria/tweet-harvest

Indonesia BEV, PHEV, and Hybrid Vehicle Total Production and Sales January – December 2023. (2024). Retrieved February 28, 2025, from GAIKINDO website: https://www.gaikindo.or.id/indonesia-bev-phev-and-hybrid-vehicle-total-production-january-december-2023/

Isnan, M., Elwirehardja, G. N., & Pardamean, B. (2023). Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model. Procedia Computer Science, 227, 168–175. https://doi.org/https://doi.org/10.1016/j.procs.2023.10.514

Jim, J. R., Talukder, M. A. R., Malakar, P., Kabir, M. M., Nur, K., & Mridha, M. F. (2024). Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review. Natural Language Processing Journal, 6(January), 100059. https://doi.org/10.1016/j.nlp.2024.100059

Jumlah Total Whole Sales Khusus Mobil BEV dan PHEV Berdasar Merek di Indonesia sepanjang Januari – Desember 2024. (2025). Retrieved February 28, 2025, from GAIKINDO website: https://www.gaikindo.or.id/jumlah-total-whole-sales-khusus-mobil-bev-dan-phev-berdasar-merek-di-indonesia-sepanjang-januari-desember-2024/

Kementrian Keuangan Republik Indonesia. (2023). Siaran Pers: Akselerasi Transformasi Ekonomi, Pemerintah Luncurkan Insentif Pembelian KBLBB Roda Empat dan Bus Per 1 April 2023. Retrieved from Kementrian Keuangan Republik Indonesia website: https://www.kemenkeu.go.id/informasi-publik/publikasi/siaran-pers/Pemerintah-Luncurkan-Insentif-Pembelian-KBLBB

Koto, F., & Rahmaningtyas, G. Y. (2017). Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs. 2017 International Conference on Asian Language Processing (IALP), 391–394. https://doi.org/10.1109/IALP.2017.8300625

Meel, P., & Vishwakarma, D. K. (2021). HAN, image captioning, and forensics ensemble multimodal fake news detection. Information Sciences, 567, 23–41. https://doi.org/10.1016/j.ins.2021.03.037

Mohd Nafis, N. S., & Awang, S. (2021). An Enhanced Hybrid Feature Selection Technique Using Term Frequency-Inverse Document Frequency and Support Vector Machine-Recursive Feature Elimination for Sentiment Classification. IEEE Access, 9, 52177–52192. https://doi.org/10.1109/ACCESS.2021.3069001

Nur Rismanah, S., Astuti, R., & M. Basysyar, F. (2024). Penerapan Algoritma Support Vector Machine Dalam Menganalisis Sentimen Ulasan Pelanggan Shopeefood Berdasarkan Twitter. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 406–412. https://doi.org/10.36040/jati.v8i1.8401

Occhipinti, A., Rogers, L., & Angione, C. (2022). A pipeline and comparative study of 12 machine learning models for text classification. Expert Systems with Applications, 201(March), 117193. https://doi.org/10.1016/j.eswa.2022.117193

Omkar, M., & Kumar, M. V. (2024). Design of Filter for Single-Phase Bidirectional Battery Charger for Electrical Vehicle Applications. International Journal on Electrical Engineering and Informatics, 16(4), 544. https://doi.org/10.15676/ijeei.2024.16.4.3

Paskahningrum, Y. K., Utami, E., & Yaqin, A. (2023). A Systematic Literature Review of Stemming in Non-Formal Indonesian Language. International Journal of Innovative Science and Research Technology, 8(1). https://doi.org/10.5281/zenodo.7547482

Pavitha, N., Pungliya, V., Raut, A., Bhonsle, R., Purohit, A., Patel, A., & Shashidhar, R. (2022). Movie recommendation and sentiment analysis using machine learning. Global Transitions Proceedings, 3(1), 279–284. https://doi.org/10.1016/j.gltp.2022.03.012

Postiglione, A. (2024). Finite State Automata on Multi-Word Units for Efficient Text-Mining †. Mathematics, 12(4). https://doi.org/10.3390/math12040506

Pratiwi, R. W., H, S. F., Dairoh, D., Af’idah, D. I., A, Q. R., & F, A. G. (2021). Analisis Sentimen Pada Review Skincare Female Daily Menggunakan Metode Support Vector Machine (SVM). Journal of Informatics, Information System, Software Engineering and Applications (INISTA), 4(1), 40–46. https://doi.org/10.20895/inista.v4i1.387

Putri, A. A. (2021). Text Pre-Processing. https://doi.org/10.1007/978-3-030-85085-2_3

Putu, N., Widiantari, T., Made, E. I., Dwi, A., Kadek, N., & Rusjayanthi, D. (2025). Food Recipe Recommendation System with Content-Based Filtering and Collaborative Filtering Methods. 9(3), 1167–1176.

Rabbani, S., Safitri, D., Rahmadhani, N., Sani, A. A. F., & Anam, M. K. (2023). Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(2), 153–160. https://doi.org/10.57152/malcom.v3i2.897

Saraswati, N. W. S., Made Krishna Muku, I. D., Dharma Suryawan, I. W., Kadek Pramita, D. A., & Agus Bisena, I. K. (2024). Balinese Temple: The Image and Characteristics of Tourists based on Sentiment Analysis. 2024 IEEE International Symposium on Consumer Technology (ISCT), 19–24. IEEE. https://doi.org/10.1109/ISCT62336.2024.10791104

Saraswati, N. W. S., Putra, I. K. G. D., Sudarma, M., & Sukarsa, I. M. (2023). The Image of Tourist Attraction in Bali Based on Big Data Analytics and Sentiment Analysis. 2023 International Conference on Smart Green Technology in Electrical and Information Systems ( ICSGTEIS ), 82–87. Denpasar, Bali: IEEE.

Saraswati, N. W. S., Putra, I. K. G. D., Sudarma, M., & Sukarsa, I. M. (2024). Enhance Sentiment Analysis in Big Data Tourism Using Hybrid Lexicon and Active Learning Support Vector Machine. In Bulletin of Electrical Engineering and Informatics.

Saraswati, N. W. S., Putra, I. K. G. D., Sudarma, M., Sukarsa, I. M., Yanti, C. P., & Tri Juniartini, N. K. (2024). Revealing the Potential of Hotel Improvements in Bali Based on Sentiment Analysis and Tourist Characteristics. International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), (September), 722–728. https://doi.org/10.1109/EECSI63442.2024.10776092

Styawati, Andi Nurkholis, Zaenal Abidin, & Heni Sulistiani. (2021). Optimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(5), 904–910. https://doi.org/10.29207/resti.v5i5.3380

Tupari, T., Abdullah, S., & Chairani, C. (2023). Visualisasi Data Analisa Sentimen RUU Omnibus Law Kesehatan Menggunakan KNN dengan Software RapidMiner. Jurnal Informatika: Jurnal Pengembangan IT, 8(3), 261–268. https://doi.org/10.30591/jpit.v8i3.5641

Xu, Q. A., Jayne, C., & Chang, V. (2024). An emoji feature-incorporated multi-view deep learning for explainable sentiment classification of social media reviews. Technological Forecasting and Social Change, 202(April 2023), 123326. https://doi.org/10.1016/j.techfore.2024.123326

Yosef Kolo, S., & Supatman, S. (2024). Analisis Sentimen Terhadap Opini Masyarakat Terkait Perubahan Cuaca Di Indonesia Menggunakan Algoritma Support Vector Machine. JATI (Jurnal Mahasiswa Teknik Informatika), 8(2), 1412–1416. https://doi.org/10.36040/jati.v8i2.8988

Zhang, Z., Liu, X., Wang, Y., Li, E., & Zhang, Y. (2025). Stability Prediction Model of Transmission Tower Slope Based on ISCSO-SVM. Electronics (Switzerland), 14(1). https://doi.org/10.3390/electronics14010126

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How to Cite

Saraswati, N. W. S., Suryawan, I. W. D. ., Muku, I. D. M. K. ., Bisena, I. K. A. ., & Pramita, D. A. K. . (2025). Indonesian Public Sentiment Toward Electric Vehicles: Analysis of Social Media Data. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(3), 1823-1834. https://doi.org/10.33395/sinkron.v9i3.15179

Issue

Vol. 9 No. 3 (2025): Article Research July 2025

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Articles

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Copyright (c) 2025 Ni Wayan Sumartini Saraswati, I Wayan Dharma Suryawan, I Dewa Made Krishna Muku, I Kadek Agus Bisena, Dewa Ayu Kadek Pramita

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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