Pengukuran Sentimen Sosial Terhadap Teknologi Kendaraan Listrik: Bukti Empiris di Indonesia

Atha Fitrah Riyadi, Faiz Ramadhani Rahman, Muhammad Aldiansyah Nofa Pratama, Muhammad Khanif Khafidli, Harry Patria


Currently, climate change due to global warming is a concern for many parties. The greenhouse gas emissions level is increasing day by day. The major contributors to Air pollution are the greenhouse effect. Transportation accounts for about 27% of air pollution, and governments in various countries use electric vehicles to reduce air pollution. However, the success of using electric vehicles is depending on perception, people’s sentiment, and understanding. The main purpose of this research is to find out how public sentiment towards electric vehicles is through tweets and comments on the Twitter social media platform using sentiment analysis. The data obtained are 1084 tweets and comments. The data were classified using the Naïve Bayes method, K-Nearest Neighbor, and Decision Tree. The results showed that the Naïve Bayes Classification Method gave better results than K-Nearest Neighbor, and Decision Tree with an accuracy rate of 94% positive sentiment by 53%, negative sentiment by 38%, and neutral sentiment by 9%. So, it can be concluded that public sentiment towards electric vehicles is quite good based on conversations on the Twitter social media platform. In addition, the author also visualizes the results of the analysis in the form of graphs and word clouds so that they can help the electric vehicle industry players to understand public sentiment better and more accurately. 


Sentiment Analysis; Electric Vehicles; Climate Change; Social Media; Analisis Sentimen; Kendaraan Listrik; Perubahan Iklim; Media Sosial;

Full Text:



Climate Transparency, “The Climate Transparency Report,” Annual G20 Report, 2020.

European Environment Agency, “Electric Vehicle in Europe,” EEA Report., no. 20/2016, 2016.

Gaikindo “Harga dan Infrastruktur Jadi Tantangan Mobil Listrik Indonesia,” 2021.

Tempo, “Menhub: Polusi Udara Disumbang Penggunaan Motor dan Mobil,” Bisnis Tempo, 2020.

Min, X., Qiang, M., & Yisi, L., “Public’s perception of adopting electric vehicles: A case study of Singapore,” Journal of the Eastern Asia Society for Transportation Studies, 12, 2017.

Christidis, P., & Focas, C., “Factors affecting the uptake of hybrid and electric ve- hicles in the European Union,” Energies, 12(18), 3414, 2019.

Graham-Rowe, E., Gardner, B., Abraham, C., Skippon, S., Dittmar, H., Hutchins, R., & Stannard, J., “Mainstream consumers driving plug-in battery-electric and plug-in hybrid electric cars: A qualitative analysis of responses and evaluations,”. Transportation Research Part A, 46, 140–153, 2012.

Schuitema, G., Anable, J., Skippon, S., & Kinnear, N., “The role of instrumental, hedonic and symbolic attributes in the intention to adopt electric vehicles,” Transportation Research Part A: Policy and Practice, 48, 39-49, 2013.

Yang, S., Deng, C., Tang, T., & Qian, Y., “Electrical vehicle’s energy consumption of car-following models,” Nonlinear Dynamics, 71, 323-329, 2013.

Sun, X., & Xu, S., “The impact of government subsidies on consumer preferences for alternative fuel vehicles,” J. Dallan Univ., Technol., (Soc. Sci), 3, 8-16, 2018.

Lane, B., & Poter, S., “The adoption of cleaner vehicles in the UK: Exploring the consumer attitude-action gap,” Journal of Cleaner Production, 15, 1085-1092, 2007.

Skippon, S., & Garwood, M., “Responses to battery electric vehicles: UK consumer attitudes and attributions of symbolic meaning following direct experience to reduce psychological distance,” Transportation Research Part D, 16, 525-531, 2011.

Jensen, A. F., Cherchi, E., & Mabit, S. L., “On the stability of preferences and attitudes before and after experiencing an electric vehicle,” Transportation Research Part D. Transport and Environment, 25, 24-32, 2013.

Wijaya, A., & Sensuse, D. I., “Perencanaan Strategis Sistem Informasi dan Teknologi Informasi Pada Perusahaan Otomotif dengan Menggunakan Metodologi Tozer,” Seminar Nasional Aplikasi Teknologi Informasi, 2011.

Databooks, “Data Publish,” Online, KataData, 2018.

Nazir, M., “Metode Penelitian,” Bogor: Ghalia Indonesia, 2017.

Mujilahwati, S., “Pre-processing Text Mining Pada Data Twitter,” Sentika. pp.49-56, 2016.

Witten. H.I and Frank.E, “Data mining Practical Machine Learning Tools and Techniques Second Edition,” Elsavier, SanFransisco, 2005.

Yousef, Ahmed Hassan, Walaa Medhat, and Hoda Korashy Mohamed. "Sentiment Analysis Algorithms and Applications: A Survey." (2014).

Muhazir, Nurul Huzna., Omar, Faizal Mohd,. Nawi, Mohd Nasrun Mohd. "Sentiment Analysis Visualization System for The Property Industry." (2018).

Alrajak, M.Suyudi., Ernawatu, Iin,. Nurlaili, Ika. "Analisis Sentimen terhadap Pelayanan PT PLN di Jakarta pada Twitter dengan Algoritma K-Nearest Neighbor (K-NN)." (2020).


Article Metrics

Abstract view : 4 times
PDF - 3 times


  • There are currently no refbacks.

EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi

Published by Pusat Studi Teknologi Informasi, Fakultas Ilmu Komputer, Universitas Bandar Lampung
Gedung M Lt.2 Pascasarjana Universitas Bandar Lampung
Jln Zainal Abidin Pagaralam No.89 Gedong Meneng, Rajabasa, Bandar Lampung,

Indexed by:

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