Sentiment Analysis of Grab Driver Motorbike User Reviews as a Basis for Service Improvement Recommendations

Authors

  • Mario Benediktus Weruin Teknik Informatika, STMIK Widya Cipta Dharma
  • Ita Arfyanti Sistem Informasi, STMIK Widya Cipta Dharma
  • Wahyuni Teknik Informatika, STMIK Widya Cipta Dharma

DOI:

https://doi.org/10.46984/aypzve37

Keywords:

Sentiment Analysis, Grab Motor, Service Quality, Lexicon-Based, User Reviews

Abstract

The development of information technology has driven the growth of application-based transportation services, one of which is Grab Motor, which is widely used by the public to meet their daily mobility needs. Service quality is an important factor influencing user satisfaction levels. One source of information that can be utilized to evaluate service quality is user reviews available on digital platforms. This study aims to analyze the sentiment of Grab Motor user reviews as a basis for developing service improvement recommendations. The data used are 200 user reviews obtained from the Google Play Store. The applied method is Lexicon-Based because it is able to identify sentiment polarity without requiring training data. The research stages include data collection, preprocessing consisting of cleansing, case folding, tokenization, normalization, and stopword removal, then continued with the assignment of sentiment scores based on the lexicon dictionary to classify reviews into positive, negative, and neutral categories. The results show that 120 reviews (60%) include positive sentiment, 50 reviews (25%) negative sentiment, and 30 reviews (15%) neutral sentiment. Model evaluation using a confusion matrix yielded an Accuracy of 89%, a Precision of 89%, a Recall of 91%, and an F1-score of 90%. The research findings indicate that the majority of users responded positively to Grab Motor's services, particularly regarding the app's ease of use and speed of service. Meanwhile, negative sentiment was still found regarding fares, pickup delays, order cancellations, and app technical issues. The results of this study are expected to serve as evaluation material and a basis for decision-making in efforts to improve service quality and Grab Motor user satisfaction.

References

Alghifari, D. R., Edi, M., & Firmansyah, L. (2022). Implementasi Bidirectional LSTM untuk analisis sentimen terhadap layanan Grab Indonesia. Jurnal Manajemen Informatika (JAMIKA), 12(2), 89–99. https://doi.org/10.34010/jamika.v12i2.7764

Dias, B., Khoirunnisa’, A., Budiman, Y., & Setiyawami. (2024). Sentiment analysis of consumer acceptance of Honda’s digital marketing strategy using lexicon-based algorithm. Journal of Information Systems and Informatics, 7(2). https://doi.org/10.51519/journalisi.v7i2.1150

Dwijayanti, M., & Hasan, F. N. (2022). Analisis sentimen pada ulasan pelanggan menggunakan metode Naïve Bayes Classifier (Studi kasus: Grab Indonesia). Prosiding Seminar Nasional Teknoka, 6, 93–99. https://doi.org/10.22236/teknoka.v6i1.441

Gunawan, A., & Setiawan, E. (2022). Evaluasi metode analisis sentimen pada data ulasan pengguna aplikasi. Jurnal Teknologi dan Sistem Komputer, 10(3), 134–141.

Hanantyo, B. S., Kridalukmana, R., & Eridani, D. (2022). Sentiment analysis pada Twitter untuk perbandingan produk secara real-time dengan menggunakan pendekatan lexicon based. Jurnal Teknik Komputer, 1(2), 41–48. https://doi.org/10.14710/jtk.v1i2.36317

Hapsari, D., & Wijaya, A. (2021). Analisis sentimen ulasan pengguna e-commerce menggunakan pendekatan text mining. Jurnal Informatika Mulawarman, 16(2), 85–92.

Maulana, F., & Hidayatullah, A. (2023). Analisis sentimen berbasis text mining terhadap ulasan layanan publik. Jurnal Sistem Informasi dan Teknologi, 8(2), 98–105.

Nurhayati, S., & Sari, D. (2020). Analisis sentimen pengguna aplikasi transportasi online di Indonesia. Jurnal Informatika, 7(1), 12–20.

Permana, A. A., & Prayuda, M. W. (2022). Penerapan metode lexicon based untuk menganalisis sentimen terhadap mudik Lebaran. Jurnal Minfo Polgan, 11(2), 137–143. https://doi.org/10.33395/jmp.v11i2.12348

Pramesti, A. D., Umam, K., & Handayani, M. R. (2025). Identification of buzzers in skincare reviews using a lexicon-based sentiment analysis method. Journal of Applied Informatics and Computing, 9(5). https://doi.org/10.30871/jaic.v9i5.11005

Pratama, A., Wibowo, A., & Santoso, B. (2020). Implementasi metode lexicon based dalam analisis sentimen ulasan pengguna. Jurnal Ilmu Komputer, 15(2), 89–96.

Putriani, E., Ramadhan, M., & Yuliana, S. (2022). Analisis sentimen aplikasi PeduliLindungi menggunakan metode KNN dan lexicon-based. Jurnal Teknologi Informasi dan Komunikasi, 11(2), 210–218.

Rahman, F., Hidayat, T., & Kurniawan, R. (2021). Penerapan text mining untuk analisis sentimen pada media sosial. Jurnal RESTI, 5(3), 567–574.

Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x

Shalehanny, S., Triayudi, A., & Handayani, E. T. E. (2021). Public’s sentiment analysis on ShopeeFood service using lexicon-based and support vector machine. Jurnal Riset Informatika, 4(1), 1–8. https://doi.org/10.34288/jri.v4i1.287

Sormin, R., Hutagalung, P., & Simanjuntak, T. (2023). Perbandingan metode lexicon-based dan Naïve Bayes dalam analisis sentimen. Jurnal Teknologi Informasi, 14(1), 67–75.

Utami, R., & Saputro, B. (2021). Analisis sentimen berbasis leksikon pada data Twitter berbahasa Indonesia. Jurnal Media Informatika, 20(1), 55–63.

Wibowo, A., Nugraha, D., & Putra, E. (2020). Pengaruh tahap preprocessing dalam analisis sentimen bahasa Indonesia. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 4(9), 2980–2987.

Widiantoro, A. D., Wibowo, A., & Harnadi, B. (2021). User sentiment analysis in the fintech OVO review based on the lexicon method. In 2021 6th International Conference on Informatics and Computing (ICIC). IEEE. https://doi.org/10.1109/ICIC54025.2021.9632909

Downloads

Published

2026-06-30

Issue

Vol. 30 No. 1 (2026): June 2026

Section

Articles

License

Copyright (c) 2026 Mario Benediktus Weruin, Ita Arfyanti, Wahyuni

Creative Commons License

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

Authors retain all their rights to the published works, such as (but not limited to) the following rights; Copyright and other proprietary rights relating to the article, such as patent rights, The right to use the substance of the article in own future works, including lectures and books, The right to reproduce the article for own purposes, The right to self-archive the article

How to Cite

“Sentiment Analysis of Grab Driver Motorbike User Reviews as a Basis for Service Improvement Recommendations” (2026) Sebatik, 30(1), pp. 204–213. doi:10.46984/aypzve37.