Measuring Public Sentiment on Electric Cars in Indonesia: A Social Media Data Analysis
DOI:
https://doi.org/10.70609/gtech.v8i4.5405Keywords:
Automotive Industry, Electric Cars, Society, Naïve bayes, Sentiment AnalysisAbstract
The Industry 4.0 era has driven rapid growth in Indonesia's automotive industry, with increasing environmental awareness fueling demand for eco-friendly vehicles, such as electric cars. In 2023, electric car sales surged by 65%, reaching 17.06 thousand units. However, widespread adoption remains hindered by high costs and limited charging infrastructure. This study aims to analyze Indonesian public sentiment toward electric cars using the Naïve Bayes algorithm. The analysis shows that positive sentiment is dominant, reflecting broad support for this innovation, despite concerns over cost and infrastructure. The Naïve Bayes model achieved an accuracy of 80%, with a precision of 84%, recall of 90%, and an F1-score of 87%. These findings can help the industry devise more effective marketing and educational strategies to encourage the adoption of electric vehicles in Indonesia.
References
Ahdiat, A. (2024). Penjualan Mobil Listrik di Indonesia Melonjak pada Akhir 2023. 2023(November 2023), 2022–2023.
Annur, C. M. (2023). Pengguna Twitter di Indonesia Capai 24 Juta hingga Awal 2023, Peringkat Berapa di Dunia? https://databoks.katadata.co.id/datapublish/2023/02/27/pengguna-twitter-di-indonesia-capai-24-juta-hingga-awal-2023-peringkat-berapa-di-dunia%0A%0A%0A%0A%0A
Armand, S., Hafid T, M., & Rafi Muttaqin, M. (2023). Analisis Sentimen Sistem E-Tilang Pada Platform Twitter Menggunakan Metode Naive Bayes. JATI (Jurnal Mahasiswa Teknik Informatika), 7(3), 1989–1994. https://doi.org/10.36040/jati.v7i3.7023
Dienwati, N., Dwilestari, G., Tohidi, E., Informatika, T., Informasi, S., Lunak, R. P., Akuntansi, K., Cirebon, K., Barat, J., Sentimen, A., Transparency, C., Neighbor, K., Machine, S. V., & Store, G. P. (2023). Penerapan Algoritma Naïve Bayes Dan K-Nearest Neighbor Untuk. 7(6), 3851–3857.
Envihsafkm. (2022). Mobil Listrik: Persoalan atau Pemecahan Masalah? https://envihsa.fkm.ui.ac.id/2022/11/25/mobil-listrik-persoalan-atau-pemecahan-masalah/
F.A, N. (2022). Propulsi Hyper Hybrid Tesla Model Y.
Fianty, Melissa Indah, Wongso, B. P., & Johan, M. E. (2023). Empowering Pregnancy Risk Assessment: A Web-Based Classification Framework with K-Means Clustering Enhanced Models. Journal of Information Systems and Informatics, 5(4). https://doi.org/10.51519/journalisi.v5i4.568
Fitrianti, I., Voutama, A., & Umaidah, Y. (2023). Clustering Film Populer Pada Aplikasi Netflix Dengan Menggunakan Algoritma K-Means Dan Metode CRISP-DM Clustering Popular Movies on Netflix App Using K-Means Algorithm and CRISP-DM Method. Jtsi, 4(2), 301–311.
Iskandar, H., & Yulanto, D. (2021). Studi Analisis Perkembangan Teknologi Kendaraan Listrik Hibrida. Journal of Automotive Technology Vocational …, 02(1), 31–44. https://journal.upy.ac.id/index.php/jatve/article/view/1488
Jati, U. S., Prabowo, D., Hastuti, D., & Gunawan, L. Van. (2024). Inspeksi Sambungan Rangka Mobil Listrik Tipe Tubular Space Frame Menggunakan Las GMAW dengan Cairan Liquid Penetrant. 15(01), 200–204. https://doi.org/10.35970/infotekmesin.v15i1.2163
Manik, G., Ernawati, I., & Nurlaili, I. (2021). Analisis Sentimen Pada Review Pengguna E-Commerce Bidang Pangan Menggunakan Metode Support Vector Machine (Studi Kasus: Review Sayurbox dan Tanihub pada Google Play). Prosiding Seminar Nasional Mahasiswa Bidang Ilmu Komputer Dan Aplikasinya, 2(2).
Nugraha, J. A., & Nusantara, U. M. (2023). Analisis Sentimen Pengguna..., Joel Alfa Nugraha, Universitas Multimedia Nusantara. 1–4.
Parinduri, L., Yusmartato, Y., & Parinduri, T. (2018). Kontribusi Konversi Mobil Konvensional ke Mobil Listrik Dalam Penanggulangan Pemanasan Global. Journal of Electrical Technology, 3(2), 116–120.
Permana, A. A., Fahrezi, M. F., Kristiyanti, D. A., & Sihotang, M. (2021). Sentimen Analisis Opini Masyarakat Pada Media Sosial Twitter Terhadap Vaksin Berbayar Menggunakan Metode Naïve Bayes Classifier (Nbc). Jurnal Teknik, 10(2), 84–92. https://doi.org/10.31000/jt.v10i2.5471
Rahmatulloh, A., & Gunawan, R. (2020). Web Scraping with HTML DOM Method for Data Collection of Scientific Articles from Google Scholar. Indonesian Journal of Information Systems, 2(2), 95–104. https://doi.org/10.24002/ijis.v2i2.3029
Raksodewanto, A. A. (2020). Membandingkan mobil listrik dengan mobil konvensional. Institut Teknologi Indonesia, 89–92.
Ramadhan, B. Z., Adam, R. I., & Maulana, I. (2022). Analisis Sentimen Ulasan pada Aplikasi E-Commerce dengan Menggunakan Algoritma Naïve Bayes. Journal of Applied Informatics and Computing, 6(2). https://doi.org/10.30871/jaic.v6i2.4725
Ramlan, R., Satyahadewi, N., & Andani, W. (2023). Analisis Sentimen Pengguna Twitter Menggunakan Support Vector Machine Pada Kasus Kenaikan Harga BBM. Jambura Journal of Mathematics, 5(2), 431–445. https://doi.org/10.34312/jjom.v5i2.20860
Rayhan, *, Widitya, A., Widitya, R. A., Satrio, F., Yuwono, P., & Saleh, M. Z. (2024). Strategi Pemasaran Mobil Konvensional dan Mobil Listrik Di Pasar Indonesia. Trending: Jurnal Ekonomi, Akuntansi Dan Manajemen, 2(1), 37–54. https://doi.org/10.30640/trending.v2i1.1910
Rifaldi, D., Abdul Fadlil, & Herman. (2023). Teknik Preprocessing Pada Text Mining Menggunakan Data Tweet “Mental Health.” Decode: Jurnal Pendidikan Teknologi Informasi, 3(2), 161–171. https://doi.org/10.51454/decode.v3i2.131
Santoso, A., Nugroho, A., & Sunge, A. S. (2022). Analisis Sentimen Tentang Mobil Listrik Dengan Metode Support Vector Machine Dan Feature Selection Particle Swarm Optimization. Journal of Practical Computer Science, 2(1), 24–31. https://doi.org/10.37366/jpcs.v2i1.1084
Satriajati, S., Panuntun, S. B., & Pramana, S. (2021). Implementasi Web Scraping Dalam Pengumpulan Berita Kriminal Pada Masa Pandemi Covid-19. Seminar Nasional Official Statistics, 2020(1), 300–308. https://doi.org/10.34123/semnasoffstat.v2020i1.578
Suhanda, Y., Kurniati, I., & Norma, S. (2020). Penerapan Metode Crisp-DM Dengan Algoritma K-Means Clustering Untuk Segmentasi Mahasiswa Berdasarkan Kualitas Akademik. Jurnal Teknologi Informatika Dan Komputer, 6(2), 12–20. https://doi.org/10.37012/jtik.v6i2.299
Tinaliah, T., & Elizabeth, T. (2022). Analisis Sentimen Ulasan Aplikasi PrimaKu Menggunakan Metode Support Vector Machine. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(4), 3436–3442. https://doi.org/10.35957/jatisi.v9i4.3586
Tulus Pangapoi Sidabutar, V. (2020). Kajian pengembangan kendaraan listrik di Indonesia: prospek dan hambatannya. Jurnal Paradigma Ekonomika, 15(1), 21–38. https://doi.org/10.22437/paradigma.v15i1.9217
Verawati, I., & Jaelani, S. N. (2024). Analisis Sentimen Pengguna Twitter Terhadap Bus Listrik Menggunakan Naïve Bayes. 8(April), 832–842. https://doi.org/10.30865/mib.v8i2.7030
Widagdo, A. S., W.A, B. S., & Nasiri, A. (2020). Analisis Tingkat Kepopuleran E-Commerce Di Indonesia Berdasarkan Sentimen Sosial Media Menggunakan Metode Naïve Bayes. Jurnal Informa : Jurnal Penelitian Dan Pengabdian Masyarakat, 6(1), 1–5. https://doi.org/10.46808/informa.v6i1.159
Yatriendi, H., Putra, A. M. N., & Muchtari, F. A. (2022). Overview: Perkembangan Teknologi Pengisian Cepat Pada Kendaraan Listrik (Teknologi dan Infrastruktur). Seminar Nasional Riset & Inovasi Teknologi, 128–137.
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