Sentiment Analysis in User Reviews of Gojek Application using Natural Language Processing
Using Orange Platform and Machine Learning Classification
Abstract
Gojek, a leading proponent of on-demand services in Indonesia, has garnered a total of 142 million downloads. However, it has received the fewest reviews compared to other on-demand applications. The objective of this research is to identify sentiment in Gojek application user reviews on Google Playstore using Natural Language Processing (NLP) approaches and machine learning algorithms through the Orange platform. The reviews utilized in this study were collected in June 2025 and encompass a total of 3,615 data points, including 2,892 training data and 723 testing data. Sentiments are classified into two categories based on their ratings: positive (rating 4-5) and negative (rating 1-2). The research process is comprised of four primary stages: data collection and labeling, text pre-processing, feature transformation using TF-IDF, and testing five classification algorithms: neural network, naïve Bayes, random forest, decision tree, and k-nearest neighbors. The evaluation results indicate that the Neural Network model demonstrates optimal performance, exhibiting 93.20% accuracy, 93.00% F1-score, and 75.80% MCC. These findings suggest that the NLP approach can be utilized effectively to comprehend user perceptions of applications. It is anticipated that this research will assist Gojek developers in the monitoring and enhancement of service quality, with this enhancement being informed by user feedback.
References
Agustina, N., Citra, D. H., Purnama, W., Nisa, C., & Kurnia, A. R. (2022). Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Ulasan Shopee pada Google Play Store: The Implementation of Naïve Bayes Algorithm for Sentiment Analysis of Shopee Reviews On Google Play Store. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 2(1), 47-54.
Alita, D., & Isnain, A. R. (2020). Pendeteksian Sarkasme pada Proses Analisis Sentimen Menggunakan Random Forest Classifier. Jurnal Komputasi, 8(2), 50-58.
Asshiddiqi, M. F., & Lhaksmana, K. M. (2020). Perbandingan Metode Decision Tree dan Support Vector Machine untuk Analisis Sentimen pada Instagram Mengenai Kinerja PSSI. eProceedings of Engineering, 7(3).
Cahyaningtyas, C., Nataliani, Y., & Widiasari, I. R. (2021). Analisis sentimen pada rating aplikasi Shopee menggunakan metode Decision Tree berbasis SMOTE. Aiti, 18(2), 173-184.
Fahmi, F., & Pratiwi, A. M. (2024). Identifying Sentiment in User Reviews of Get Contact Application using Natural Language Processing. Proceedings - 2024 International of Seminar on Application for Technology of Information and Communication: Smart And Emerging Technology for a Better Life, ISemantic 2024, 428–433. https://doi.org/10.1109/iSemantic63362.2024.10762453
Fitra Ramadhan, F. (2021). Chatbot pada E-Commerce berbasis Android dengan Pendekatan Natural Language Processing. JCSE Journal of Computer Science an Engineering, 2(1), 27–39. http://icsejournal.com/index.php/
Husain, N. P., Sukirman, S., & SAJIAH, S. (2024). Analisis Sentimen Ulasan Pengguna Tiktok pada Google Play Store Berbasis TF-IDF dan Support Vector Machine. Journal of System and Computer Engineering (JSCE), 5(1), 91–102. https://doi.org/10.61628/jsce.v5i1.1105
Istiqamah, N., & Rijal, M. (2024). Klasifikasi Ulasan Konsumen Menggunakan Random Forest dan SMOTE. Journal of System and Computer Engineering (JSCE), 5(1), 66–77. https://doi.org/10.61628/jsce.v5i1.1061
Katadata. 2019. Persaingan Ketat Gojek dan Grab Menjadi SuperApp. https://katadata.co.id/telaah/2019/04/16/persaingan-ketat-gojek-dan-grab-menjadi-superapp.
Katadata. 2025. Gojek, Grab, dan Maxim, Mana yang Punya Sentimen Paling Positif? https://databoks.katadata.co.id/transportasi-logistik/statistik/682c48bcd8785/gojek-grab-dan-maxim-mana-yang-punya-sentimen-paling-positif
Liu, G., Zhao, H., Fan, F., Liu, G., Xu, Q., & Nazir, S. (2022). An Enhanced Intrusion Detection Model Based on Improved kNN in WSNs. Sensors, 22(4), 1407. https://doi.org/10.3390/s22041407
Maulita, I., & Wahid, A. (2024). Prediksi Magnitudo Gempa Menggunakan Random Forest, Support Vector Regression, XGBoost, LightGBM, dan Multi-Layer Perceptron Berdasarkan Data Kedalaman dan Geolokasi (Predicting Earthquake Magnitude Using Random Forest, Support Vector Regression, XGBoost, LightGBM, and Multi-Layer Perceptron Based on Depth and Geolocation Data). Jurnal Pendidikan Dan Teknologi Indonesia, 4, 221-232.
Mienye, I. D., & Swart, T. G. (2024). A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications. Information, 15(12), 755. https://doi.org/10.3390/info15120755
Muttaqin, M. N., Kharisudin, I. 2021. Analisis Sentimen Pada Ulasan Aplikasi Gojek Menggunakan Metode Support Vector Machine dan K Nearest Neighbor. UNNES Journal of Mathematics. 10(2):22-27.
Rahayu, S., Mz, Y., Bororing, J. E., & Hadiyat, R. (2022). Implementasi Metode K-Nearest Neighbor (K-NN) untuk Analisis Sentimen Kepuasan Pengguna Aplikasi Teknologi Finansial FLIP. Edumatic J. Pendidik. Inform, 6(1), 98-106.
Rahman, R. A., Pranatawijaya, V. H., & Sari, N. N. K. (2023). Analisis Sentimen Berbasis Aspek pada Ulasan Aplikasi Gojek. KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, 4(1). https://doi.org/10.24002/konstelasi.v4i1.8922
Rimba, Chory Nuzulul. (2019). Analisis Sentimen pada tingkat kepuasan pengguna layanan data seluler menggunakan algoritma Support Vector Machine(SVM).
Sarker, I. H. (2021). Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN computer science, 2(6), 1-20.
Syafrianto, A. (2022). Perbandingan Algoritma Naïve Bayes dan Decision Tree Pada Sentimen Analisis. The Indonesian Journal of Computer Science Research, 1(2).
Tatchum, G. W., Nzeko’o, A. J. N., Makembe, F. S., & Djam, X. Y. (2024). Class-Oriented Text Vectorization for Text Classification: Case Study of Job Offer Classification. Journal of Computer Science and Engineering (JCSE), 5(2), 116–136.
Wahyuni, W. (2022). Analisis Sentimen terhadap Opini Feminisme Menggunakan Metode Naive Bayes. Jurnal Informatika Ekonomi Bisnis, 148-153.
Wardhani, D., Astuti, R., & Saputra, D. D. (2024). Optimasi Feature Selection Text Mining: Stemming Dan Stopword Untuk Sentimen Analisis Aplikasi SatuSehat. Innovative: Journal Of Social Science Research, 4(1), 7537-7548.
