SENTIMENT ANALYSIS OF PUBLIC OPINION ON TRANSPORTATION SERVICES IN INDONESIA USING MACHINE LEARNING
DOI:
https://doi.org/10.33480/techno.v20i2.6577Keywords:
Naïve Bayes, Sentiment Analysis , Support Vector Machine , Transportation , TwitterAbstract
This study analyzes public sentiment towards transportation services in Indonesia through social media using Naïve Bayes and Support Vector Machine (SVM) algorithms. Data was collected from Twitter using an API with transportation-related keywords over a three-month period. The analysis results indicate that 93.5% of the opinions are neutral, 3.5% are positive, and 3% are negative. The dominance of neutral sentiment suggests potential dataset imbalance or user hesitation in expressing strong opinions. SVM achieved a higher accuracy (100%) compared to Naïve Bayes (92%), which may be influenced by dataset limitations or the model's validation method. Data preprocessing involved several steps, including tokenization, stopword removal, stemming, lemmatization, and handling of missing data to ensure cleaner and more structured text input. These findings highlight the potential of sentiment analysis for transportation policy improvements, providing insights for policymakers and transport service providers. Future research should address data balancing and broader dataset usage to enhance the robustness of findings and support better decision-making in the transportation sector.
References
A. Pratama & T. W. Hadi. (2022). Utilizing Social Media as a Data Source for Analyzing Public Perception of Public Transportation. Journal of Information Systems, 10, n.
D. Kusuma et al. (2021). Implementation of Machine Learning in Public Sentiment Analysis of App-Based Transportation Services. Journal of Information Technology, 18, n.
Dwianto, E., & Sadikin, M. (2021). Sentiment Analysis of Online Transportation on Twitter Using Naïve Bayes and Support Vector Machine Classification Methods. Format: Scientific Journal of Informatics Engineering, 10(1), 94. https://doi.org/10.22441/format.2021.v10.i1.009
Fadlisyah, F., & Muhathir, M. (2023). Performance Evaluation Of Variations Boosting Algorithms For Classifying Formalin Fish From Photos. Journal of Informatics and Telecommunication Engineering, 6(2), 621–629. https://doi.org/10.31289/jite.v6i2.6614
Geni, L., Yulianti, E., & Sensuse, D. I. (2023). Sentiment Analysis of Tweets Before the 2024 Elections in Indonesia Using IndoBERT Language Models. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika (JITEKI), 9(3), 746–757. https://doi.org/10.26555/jiteki.v9i3.26490
Hidayati, I. (2023). Breaking the commute barrier: How women in Jabodetabek overcome daily challenges on commuting for work. ETNOSIA : Jurnal Etnografi Indonesia, 8(1), 44–62. https://doi.org/10.31947/etnosia.v8i1.26085
Iwandini, I., Triayudi, A., & Soepriyono, G. (2023). Sentiment Analysis of Jakarta Transportation Users Towards TransJakarta Using Naïve Bayes and K-Nearest Neighbor Methods. Journal of Information System Research (JOSH), 4(2), 543–550. https://doi.org/10.47065/josh.v4i2.2937
Khairunnisa, S., Adiwijaya, A., & Faraby, S. Al. (2021). Effect of Text Preprocessing on Sentiment Analysis of Public Comments on Twitter (Case Study: COVID-19 Pandemic). Journal of Media Informatics Budidarma, 5(2), 406. https://doi.org/10.30865/mib.v5i2.2835
Khoiruddin, Y., Fauzi, A., & Siregar, A. M. (2023). Sentiment Analysis of Gojek Indonesia on Twitter Using Naïve Bayes and Support Vector Machine Algorithms. Scientific Journal of Computer Science, 19, 391–400.
M. Yusuf & L. Kurniawan. (2023). Comparison of Support Vector Machine and Deep Learning Algorithms in Online Transportation Sentiment Analysis. Journal of Artificial Intelligence, 5, no.
N. Dewi & P. Santoso. (2022). Analysis of Public Opinion Trends on Public Transportation Through Social Media. Journal of Computer Science, 12, n.
Nalle, V. I. W., Syaputri, M. D., Krisnanto, W., & Tjandra, O. C. P. (2023). Public Participation in Bus Transit Policymaking: The Case of Semarang, Indonesia. International Journal of Transport Development and Integration, 7(3), 235–245. https://doi.org/10.18280/ijtdi.070307
Novaneliza, R., Handayani, F., Suhandar, R. J., Surono, H., Azzahra, N. S., & Nadilla, D. (2023). Comparison of Algorithms for Sentiment Analysis on Twitter for Public Transportation Commuter Line. Journal of Computer Science & Informatics (J-SAKTI, 7(1), 13–21.
Pratama, D., Rifai, A. I., & Handayani, S. (2023). The Passenger Satisfaction Analysis of Commuter Line in the New-Normal Period. Indonesian Journal of Multidisciplinary Science, 1(1), 409–418. https://doi.org/10.55324/ijoms.v1i1.398
R. Sari & B. Nugroho. (2021). Sentiment Analysis of Online Transportation Users on Twitter Using Naïve Bayes Method. Journal of Informatics, 14, n.
Said, L. B., & Syafey, I. (2022). User acceptance oF public transport systems based on a perception model. International Journal of Transport Development and Integration, 6(4), 399–414. https://doi.org/10.2495/TDI-V6-N4-399-414
Savita, D. A., Putra, I. K. G. D., & Rusjayanthi, N. K. D. (2021). Public Sentiment Analysis of Online Transportation in Indonesia through Social Media Using Google Machine Learning. Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi), 9(2), 153. https://doi.org/10.24843/jim.2021.v09.i02.p06
Setiawan, Y., Jondri, J., & Astuti, W. (2022). Twitter Sentiment Analysis on Online Transportation in Indonesia Using Ensemble Stacking. Jurnal Media Informatika Budidarma, 6(3), 1452. https://doi.org/10.30865/mib.v6i3.4359
Singgalen, Y. A. (2021). Selection of Methods and Algorithms in Sentiment Analysis on Social Media: Systematic Literature Review. Journal of Information Systems and Informatics, 3(2), 278–302. https://doi.org/10.33557/journalisi.v3i2.125
T. Ramadhani & R. Widodo. (2021). Twitter-Based Sentiment Analysis for Public Transportation Service Evaluation. Journal of Data Science, 6, no.
Wijiyanto, W., Pradana, A. I., Sopingi, S., & Atina, V. (2024). K-Fold Cross Validation Technique for Evaluating Student Performance. Journal of Algorithms, 21(1), 239–248. https://doi.org/10.33364/algoritma/v.21-1.1618
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Fina Sifaul Nufus, Windu Gata

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The copyright of any article in the TECHNO Nusa Mandiri Journal is fully held by the author under the Creative Commons CC BY-NC license. The copyright in each article belongs to the author. Authors retain all their rights to published works, not limited to the rights set out on this page. The author acknowledges that Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) is the first to publish with a Creative Commons Attribution 4.0 International license (CC BY-NC). Authors can enter articles separately, manage non-exclusive distribution, from manuscripts that have been published in this journal into another version (for example: sent to author affiliation respository, publication into books, etc.), by acknowledging that the manuscript was published for the first time in Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri); The author guarantees that the original article, written by the stated author, has never been published before, does not contain any statements that violate the law, does not violate the rights of others, is subject to the copyright which is exclusively held by the author. If an article was prepared jointly by more than one author, each author submitting the manuscript warrants that he has been authorized by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agrees to notify the co-authors of the terms of this policy. Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) will not be held responsible for anything that may have occurred due to the author's internal disputes.