Sentiment Analysis and Classification of User Reviews of the 'Access by KAI' Application Using Machine Learning Methods to Improve Service Quality
DOI:
https://doi.org/10.51519/journalisi.v7i2.1099Keywords:
Analysis Sentiment, Machine Learning, Access by KAI, Service Quality, Speed, Payment, UI/UXAbstract
This research applies sentiment analysis to understand user perceptions of the Access by KAI application, especially specific aspects such as speed, payment process, and user interface (UI/UX). User reviews are collected and processed through preprocessing stages, balancing using the SMOTE method, and classified using three machine learning algorithms, namely Support Vector Machine (SVM), Decision Tree, and Logistic Regression. The SVM model achieved the highest accuracy of 89.33%, followed by Logistic Regression at 88%, and Decision Tree at 86.67%. Precision, recall, and F1-scores for each model were also evaluated, showing strong performance in detecting negative sentiments but lower performance for neutral and positive sentiments. In addition, keyword-based analysis revealed that negative sentiment was most commonly found in the aspects of the payment process and speed. WordCloud visualization also strengthens the results by showing the dominance of negative words in user reviews. The results of this study provide important suggestions and input for application developers to improve aspects of the service that are considered less satisfactory by users. Thus, this study can be used as a practical guide in making strategic decisions to improve the quality of service and user satisfaction of the Access by KAI application.
Downloads
References
G. Radiena and A. Nugroho, “Analisis Sentimen Berbasis Aspek Pada Ulasan Aplikasi KAI Access Menggunakan Metode Support Vector Machine,” 2023.
H. Indrawan, B. Irawan, and T. Suprapti, “Klasifikasi Ulasan Pengguna Aplikasi Access By KAI Berbasis Aspek Dengan Algoritma Naïve Bayes Dan SVM,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 6, pp. 3541–3548, 2023.
D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” 2021.
A. U. Haspriyanti and P. W. Prasetyaningrum, “Penerapan Data Mining Untuk Prediksi Layanan Produk Indihome Menggunakan Metode K-Nearst Neighbor,” 2021.
Irbah salsabila and Yuliant Sibaroni, “Multi Aspect Sentiment of Beauty Product Reviews using SVM and Semantic Similarity,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 3, pp. 520–526, Jun. 2021, doi: 10.29207/resti.v5i3.3078.
W. Wahyuningsih and P. T. Prasetyaningrum, “Enhancing Sales Determination for Coffee Shop Packages through Associated Data Mining: Leveraging the FP-Growth Algorithm,” Journal of Information Systems and Informatics, vol. 5, no. 2, pp. 758–770, May 2023, doi: 10.51519/journalisi.v5i2.500.
M. Oktafani and P. T. Prasetyaningrum, “Implementasi Support Vector Machine Untuk Analisis Sentimen Komentar Aplikasi Tanda Tangan Digital,” Jurnal Sistem Informasi dan Bisnis Cerdas, vol. 15, no. 1, Mar. 2022, doi: 10.33005/sibc.v15i1.2697.
M. Rizqi, A. Rustiawan, and P. T. Prasetyaningrum, “Analisis Sentimen Terhadap Klinik Natasha Skincare di Yogyakarta Dengan Metode Google Review,” Journal of Information Technology Ampera, vol. 5, no. 1, pp. 2774–2121, 2024, doi: 10.51519/journalita.v5i1.556.
P. T. Prasetyaningrum, P. Purwanto, and A. F. Rochim, “Consumer Behavior Analysis in Gamified Mobile Banking: Clustering and Classifier Evaluation,” Online) Journal of System and Management Sciences, vol. 15, no. 2, pp. 290–308, 2025, doi: 10.33168/JSMS.2025.0218.
M. Windarti and P. T. Prasetyaninrum, “Prediction Analysis Student Graduate Using Multilayer Perceptron,” 2020.
P. T. Prasetyaningrum, A. R. Wicaksono, and H. Nurrofiq, “Transformasi Pelayanan Pelanggan: Implementasi E-CRM Pada Bisnis Teh Nusantara Berbasis Website,” Technologia : Jurnal Ilmiah, vol. 14, no. 4, p. 368, Oct. 2023, doi: 10.31602/tji.v14i4.12157.
P. T. Prasetyaningrum, I. Pratama, N. T. Kadir, and A. Y. Chandra, “Comparison Of Support Vector Machine Radial Base And Linear Kernel Functions For Mobile Banking Customer Satisfaction Analysis.”
M. A. S. Nugroho, D. Susilo, and D. Retnoningsih, “Analisis Sentimen Ulasan Aplikasi ”Access By KAI” Menggunakan Algoritma Machine Learning,” Jurnal Teknik Informasi dan Komputer (Tekinkom), vol. 7, no. 2, p. 820, Dec. 2024, doi: 10.37600/tekinkom.v7i2.1854.
R. Damanhuri and V. A. Husein, “Analisis Sentimen pada Ulasan Aplikasi Access by KAI Berbahasa Indonesia Menggunakan Word-Embedding dan Classical Machine Learning,” Jurnal Masyarakat Informatika, vol. 15, no. 2, pp. 97–106, Nov. 2024, doi: 10.14710/jmasif.15.2.62383.
N. D. Septiyanti, M. I. Luthfi, and N. T. Romadloni, “Komparasi Metode Klasifikasi Dalam Analisis Sentimen Ulasan Pengguna Aplikasi KRL Access Di Google Play Store,” Journal Computer Science and Information Systems : J-Cosys, vol. 4, no. 1, pp. 64–75, Mar. 2024, doi: 10.53514/jco.v4i1.495.
A. Rhamadanti, A. Rifa’i, F. Dikananda, and K. Anam, “Analisis Sentimen Pada Ulasan Access By Kereta Api Indonesia Dengan K-Nearest Neighbor,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 1, pp. 2830–7062, doi: 10.23960/jitet.v12i1.3691.
A. A. Muhammad, E. Ermatita, and D. S. Prasvita, “Analisis Sentimen Pengguna Aplikasi Dana Berdasarkan Ulasan pada Google Play Menggunakan Metode Support Vector Machine,” in Prosiding Seminar Nasional Mahasiswa Bidang Ilmu Komputer dan Aplikasinya, 2022, pp. 194–204.
N. B. Sidauruk and N. Riza, “Sentimen Analisis Data Pengguna Terhadap KAI Access Systematic Literature Review,” 2023.
H. Mustakim and S. Priyanta, “Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 16, no. 2, p. 113, Apr. 2022, doi: 10.22146/ijccs.68903.
Y. Sibaroni, “Multi aspect sentiment of beauty product reviews using SVM and semantic similarity,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 3, pp. 520–526, 2021.
O. S. D. Fadhillah, J. H. Jaman, and C. Carudin, “Perbandingan Naive Bayes, Support Vector Machine, Logistic Regression Dan Random Forest Dalam Menganalisis Sentimen Mengenai Tiktokshop,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 13, no. 1, Jan. 2025, doi: 10.23960/jitet.v13i1.5746.
N. C. Ramadani, “Analisis Sentimen Untuk Mengukur Ulasan Pengguna Aplikasi Mobile Legend Menggunakan Algoritma Naive Bayes, SVM, Random Fores, Decision Tree, dan Logistic Regression,” JSI : Jurnal Sistem Informasi (E-Journal, vol. 16, no. 1, 2024.
M. A. S. Nugroho, D. Susilo, and D. Retnoningsih, “Analisis Sentimen Ulasan Aplikasi ”Access By KAI” Menggunakan Algoritma Machine Learning,” Jurnal Teknik Informasi dan Komputer (Tekinkom), vol. 7, no. 2, p. 820, Dec. 2024, doi: 10.37600/tekinkom.v7i2.1854.
S. D. Alyusi and I. Yuadi, “Implementation Of Machine Learning In Improving Website User Experience And Satisfaction,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 10, no. 1, pp. 781–788, Mar. 2025, doi: 10.29100/jipi.v10i1.7439.
R. A. Hidayah, R. Insani, and B. R. Lidiawaty, “Sentiment Classification of User Reviews for KAI Access Application Using Naive Bayes Method,” Journal of Dinda Data Science, Information Technology, and Data Analytics, vol. 4, no. 2, pp. 91–97, 2024.
Downloads
Published
Issue
Section
License
Authors Declaration
- The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
- The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
- The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
- The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
- The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
- If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
- The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights
