SENTIMENT ANALYSIS OF JAKLINGKO APP REVIEWS USING MACHINE LEARNING AND LSTM
DOI:
https://doi.org/10.33480/techno.v22i1.6375Keywords:
Jaklingko, Machine Learning, Sentiment Analysis, Text Mining, User ReviewsAbstract
Application-based transportation services have rapidly developed in recent years, with various studies indicating that service quality and user experience play a crucial role in the adoption of this technology. Previous research has analyzed user satisfaction with digital transportation applications, highlighting factors such as ease of use, service reliability, and the effectiveness of fare systems. This study aims to analyze user sentiment toward the JakLingko application to assess satisfaction levels and identify aspects that need improvement. Utilizing a dataset of 200 user reviews, this research applies data preprocessing techniques to clean and organize the information before performing sentiment classification. The machine learning models used include Naïve Bayes, Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Long Short-Term Memory (LSTM), categorizing sentiment into positive, negative, and neutral. The analysis results indicate a dominance of negative sentiment in user reviews, reflecting a significant level of dissatisfaction with the application. This highlights major challenges in the implementation of transportation applications, potentially affecting public adoption and trust in the service. Therefore, besides providing insights into user perceptions, this study also proposes improvement strategies aimed at enhancing features and the overall user experience. Given the high proportion of negative sentiment, this research emphasizes the importance of improving the accuracy of sentiment analysis models to generate deeper and more precise insights. These findings can serve as a foundation for designing policies and strategies to improve application-based transportation services, ultimately enhancing service quality and expanding user adoption.
References
Abbas Nawar Khalifa, Hadi Raheem Ali, A. J., & Jebur, S. A. (2024). Automate Facial Paralysis Detection Using VGG Architectures. In International Journal of Current Innovations in Advanced Research. https://doi.org/10.47957/ijciar.v7i1.158
Adinegoro, K. R. R. (2022). Implementasi Sikap Kolaboratif dan Multikultural dalam Kepemimpinan pada Integrasi dan Penataan Transportasi Umum “JAK LINGKO” di DKI Jakarta. Spirit Publik, 17(1). https://doi.org/10.20961/sp.v17i1.57666
Agoestanto, P. A. and A. (2024). Modeling of Naïve Bayes and Decision Tree Algorithms to Analyze Sentiment Related to Jaklingko Public Transportation on Social Media X (Twitter). Pattimura Proceeding: Conference of Science and Technology, 5(1), 67–78. https://doi.org/10.30598/ppcst.knmxxiiv5i1p67-78
Bikbov, B., Purcell, C. A., Levey, A. S., Smith, M., Abdoli, A., Abebe, M., Adebayo, O., Afarideh, M., Agarwal, S. K., Agudelo-Botero, M., Ahmadian, E., Al‐Aly, Z., Alipour, V., Almasi‐Hashiani, A., Al-Raddadi, R., Alvis‐Guzmán, N., Amini, S., Andrei, T., Andrei, C. L., … Murray, C. (2020). Global, Regional, and National Burden of Chronic Kidney Disease, 1990–2017: A Systematic Analysis for the Global Burden of Disease Study 2017. In The Lancet. https://doi.org/10.1016/s0140-6736(20)30045-3
Cascia, M. S. and I. T. and M. La. (2023). Is text preprocessing still worth the time? A comparative survey on the influence of popular preprocessing methods on Transformers and traditional classifiers. Information Systems.
Gary Ekatama Bangun, M. S. (n.d.). Evaluasi kebijakan integrasi angkutan pengumpan ke dalam sistem bus rapid transit: Studi pada Mikrotrans Jaklingko. 2024. https://doi.org/10.30738/sosio.v10i1.16268
Hanifa Aulia Rahma, Slamet Rosyadi, Guntur Gunarto, S. (2024). Collaborative Governance in Management of the JakLingko Program (Case Study on Public Transportation Management in DKI Jakarta). https://doi.org/10.47134/jebmi.v2i1.139
Idris, Y. A. M. and I. S. K. (2024). Ensemble Approach to Sentiment Analysis of Google Play Store App Reviews. Jambura Journal of Electrical and Electronics Engineering, 6(Universitas Negeri Gorontalo), 181–188. https://doi.org/10.37905/jjeee.v6i2.25184
Ladayya, Faroh and Siregar, Dania and Pranoto, Wiligis Eka and Muchtar, H. D. (2022). Analisis Sentimen pada Program Transportasi Publik JakLingko dengan Metode Support Vector Machine. Jurnal Statistika Dan Aplikasinya, 6(2), 381–392.
Liebenlito, D. A. A. and T. E. S. and M. (2024). Confident Learning pada IndoBERT: Peningkatan Kinerja Klasifikasi Sentimen. Indonesian Journal of Computer Science, 13(STMIK Indonesia Padang), 5. https://doi.org/10.33022/ijcs.v13i5.4401
Mishra, A. S. and G. D. and A. J. and A. (2024). Machine Learning Algorithms and Applications. Auerbach Publications, 1–31. https://doi.org/10.1201/9781003504900-1
Mishra, P. (2022). Bypassing NIR pre-processing optimization with multiblock pre-processing ensemble approaches. Nir News, 33, 5–8. https://doi.org/10.1177/09603360221139227
Nugraheni, T. C. P. and W. W. and M. (2024). Indonesian Fake News Classification Using Transfer Learning in CNN and LSTM. JOIV : International Journal on Informatics Visualization, 8(State Polytechnics of Andalas), 1213–1213. https://doi.org/10.62527/joiv.8.2.2126
Putranto, L. S. (2023). Persepsi pengguna transportasi umum di jabodetabek terhadap integrasi tarif pt jaklingko indonesia. Jurnal Mitra Teknik Sipil, 71–84. https://doi.org/10.24912/jmts.v6i1.16430
Sabri, S. M. and M. M. and W. A. W. A. B. and M. K. Y. and I. A. A. (2024). Demystified Overview of Data Scraping. 6, 290–296. https://doi.org/10.69511/ijdsaa.v6i6.205
Su, Y. H. and Y.-D. H. and N. (2024). Performance of tests based on the area under the ROC curve for multireader diagnostic data. Journal of Applied Statistic, Taylor and Francis, 1–23. https://doi.org/10.1080/02664763.2024.2374931
Suryawanshi, N. (2024). Sentiment analysis with machine learning and deep learning: A survey of techniques and applications. International Journal of Science and Research Archive, 12(2), 005–015. https://doi.org/10.30574/ijsra.2024.12.2.1205
Vikkram, V. N. and S. L. and A. S. and R. (2024). Deep Learning. Auerbach Publications, Auerbach Publ., 1–8. https://doi.org/10.1201/9781003433309-1
Yusuf, M. I. A. C. and R. (2024). Visualisasi kata kunci pemberitaan pemilu 2024 menggunakan scapy dan wordcloud. Jurnal TEKNIMEDIA, 5(STMIK Syaikh Zainuddin NW Anjani-Lombok Timur), 41–46. https://doi.org/10.46764/teknimedia.v5i1.187
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