Klasifikasi Sentimen Tweet Masyarakat terhadap Kendaraan Listrik Menggunakan Support Vector Machine

Authors

  • Nuari Ananda Universitas Islam Negeri Sultan Syarif Kasim Riau http://orcid.org/0009-0007-2944-6523
  • Muhammad Fikry Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Yusra Yusra Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Lestari Handayani Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Iwan Iskandar Universitas Islam Negeri Sultan Syarif Kasim Riau

DOI:

https://doi.org/10.32493/informatika.v8i4.36754

Keywords:

sentiment analysis, electric vehicles, Support Vector Machine, negation

Abstract

Sentiment analysis involves using classification algorithms to analyze public opinions and feelings in text. Within the automobile industry, electric vehicles (EVs) stem from the circular economy and represent a novel technology under investigation in sentiment classification studies. The Support Vector Machine (SVM) algorithm is commonly used in this research due to its superior accuracy compared to other algorithms. The goal of this study is to apply SVM variable selection techniques to enhance sentiment analysis quality. Python is the programming language used to build the sentiment classification model, which involves feature selection using TF-IDF, training with cross-validation and grid search, evaluation using a confusion matrix, and storing the dataset in a MySQL database. The research focuses on the sentiment classification of 3000 public tweets about electric vehicles on Twitter. Through various scenarios, it was observed that the accuracy of sentiment classification varied depending on factors such as randomizing data, handling negation, and using different types of features like unigrams or bigrams. The highest accuracy achieved was 84% using a scenario with random data, negation handling, and unigram features. Overall, this research highlights the impact of randomizing data and selecting appropriate features on sentiment classification accuracy for electric vehicles on Twitter.

Author Biography

Nuari Ananda, Universitas Islam Negeri Sultan Syarif Kasim Riau

I am nuari ananda student majoring in infomatics engineering at sultan syarif kasim riau state islamic university, who is conducting research on the topic of NLP for my requirement to get a strata 1 degree.

References

Amrizal, V. (2018). Penerapan Metode Term Frequency Inverse Document Frequency (Tf-Idf) Dan Cosine Similarity Pada Sistem Temu Kembali Informasi Untuk Mengetahui Syarah Hadits Berbasis Web (Studi Kasus: Hadits Shahih Bukhari-Muslim). Jurnal Teknik Informatika, 11(2), 149–164. https://doi.org/10.15408/jti.v11i2.8623

Athira Luqyana, W., Cholissodin, I., & Perdana, R. S. (2018). Analisis Sentimen Cyberbullying pada Komentar Instagram dengan Metode Klasifikasi Support Vector Machine. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(11), 4704–4713. http://j-ptiik.ub.ac.id

Bhatnagar, S., & Choubey, N. (2021). Making sense of tweets using sentiment analysis on closely related topics. Social Network Analysis and Mining, 11(1). https://doi.org/10.1007/s13278-021-00752-0

Costello, F. J., & Lee, K. C. (2020). Exploring the Sentiment Analysis of Electric Vehicles Social Media Data by Using Feature Selection Methods. Journal of Digital Convergence, 18(2), 249–259. https://doi.org/10.14400/JDC.2020.18.2.249

Geissdoerfer, M., Pieroni, M. P. P., Pigosso, D. C. A., & Soufani, K. (2020). Circular business models: A review. Journal of Cleaner Production, 277(August). https://doi.org/10.1016/j.jclepro.2020.123741

German Association of the Automotive Industry. (2021). Pkw-Produktion deutscher Hersteller in Deutschland. https://www.vda.de/de

Handayani, F., & Mustikasari, M. (2020). Sentiment Analysis of Electric Cars Using Recurrent Neural Network Method in Indonesian Tweets. Jurnal Ilmiah Kursor, 10(4), 153–158. https://doi.org/10.21107/kursor.v10i4.233

Impressum. (2021). 2020 (Full Year) International: Worldwide Car Sales. https://www.best-selling-cars.com/international/2020-full-year-international-worldwide-car-sales/

Jefferson, O. A., Jaffe, A., Ashton, D., Warren, B., Koellhofer, D., Dulleck, U., Ballagh, A., Moe, J., Dicuccio, M., Ward, K., Bilder, G., Dolby, K., & Jefferson, R. A. (2018). Erratum: Mapping the global influence of published research on industry and innovation (Nat. Biotechnol., 2018 36, 31–39). Nature Biotechnology, 36(8), 772. https://doi.org/10.1038/NBT0818-772A

Mauleón, I. (2021). Aggregated world energy demand projections: Statistical assessment. Energies, 14(15). https://doi.org/10.3390/en14154657

Maulidina, M. K. (2020). Analisis Sentimen Komentar Warganet Terhadap Postingan Instagram Menggunakan Metode Naive Bayes Classifier dan TF-IDF. Naskah Publikasi Universitas Teknologi Yogyakarta, 1–15.

Nai, W., Yang, Z., Wei, Y., Sang, J., Wang, J., Wang, Z., & Mo, P. (2022). A Comprehensive Review of Driving Style Evaluation Approaches and Product Designs Applied to Vehicle Usage-Based Insurance. Sustainability (Switzerland), 14(13). https://doi.org/10.3390/su14137705

Nasution, M. R. A., & Hayaty, M. (2019). Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter. Jurnal Informatika, 6(2), 226–235. https://doi.org/10.31311/ji.v6i2.5129

Olabi, A. G., Wilberforce, T., Sayed, E. T., Elsaid, K., & Abdelkareem, M. A. (2020). Prospects of fuel cell combined heat and power systems. Energies, 13(15), 1–20. https://doi.org/10.3390/en13164104

Renas Madya Pradhana. (2021). Analisis Sentimen Publik Terhadap Kebijakan Pemberlakuan Pembatasan Kegiatan Masyarakat Skala Mikro Menggunakan Algoritma Support Vector Machine Studi Kasus Twitter. Universitas Dinamika, 10(4), 66.

Rivki, M., & Bachtiar, A. M. (2017). IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR DALAM PENGKLASIFIKASIAN FOLLOWER TWITTER YANG MENGGUNAKAN BAHASA INDONESIA. Jurnal Sistem Informasi (Journal of Information System), 13(1), 1–23.

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

Septiani, R., Citra, I. P. A., & Nugraha, A. S. A. (2019). Perbandingan Metode Supervised Classification dan Unsupervised Classification terhadap Penutup Lahan di Kabupaten Buleleng. Jurnal Geografi : Media Informasi Pengembangan Dan Profesi Kegeografian, 16(2), 90–96. https://doi.org/10.15294/jg.v16i2.19777

Supriyati, E., & Iqbal, M. (2018). Pengukuran Similarity Tema Pada Juz 30 Al Qur’an Menggunakan Teks Klasifikasi. Simetris: Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 9(1), 361–370. https://doi.org/10.24176/simet.v9i1.1955

Vongurai, R. (2020). Factors affecting customer brand preference toward electric vehicle in Bangkok, Thailand. Journal of Asian Finance, Economics and Business, 7(8), 383–393. https://doi.org/10.13106/JAFEB.2020.VOL7.NO8.383

Welsby, D., Price, J., Pye, S., & Ekins, P. (2021). Unextractable fossil fuels in a 1.5 °C world. Nature, 597(7875), 230–234. https://doi.org/10.1038/s41586-021-03821-8

Downloads

Published

2023-12-28

Issue

Vol. 8 No. 4 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG

Section

Article

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

Jurnal Informatika Universitas Pamulang have CC-BY-NC or an equivalent license as the optimal license for the publication, distribution, use, and reuse of scholarly work.

In developing strategy and setting priorities, Jurnal Informatika Universitas Pamulang recognize that free access is better than priced access, libre access is better than free access, and libre under CC-BY-NC or the equivalent is better than libre under more restrictive open licenses. We should achieve what we can when we can. We should not delay achieving free in order to achieve libre, and we should not stop with free when we can achieve libre.

Jurnal Informatika Universitas Pamulang is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

YOU ARE FREE TO:

  • Adapt : remix, transform, and build upon the material for any purpose, even commercially.
  • The licensor cannot revoke these freedoms as long as you follow the license terms