Analisis Sentimen Ulasan Pengguna Aplikasi MyTelkomsel Menggunakan TF-IDF dan Random Forest
-
Rizki Ariyani
*
Universitas Gunadarma, Depok,
Indonesia
- Masimbangan Sabarina Harlina Universitas Gunadarma, Depok, Indonesia
- Masimbangan Susana Herawati Universitas Gunadarma, Depok, Indonesia (*) Corresponding Author
Abstract
The development of digital services in the telecommunications sector encourages service providers to continuously improve the quality of applications used by customers. The MyTelkomsel application has become one of the main media for users to access various Telkomsel services, so that reviews provided by users on the Google Play Store can be an important source of information to determine the level of satisfaction and problems experienced by users. However, the increasing number of reviews makes the manual analysis process less effective, takes a long time, and has the potential to produce subjective assessments. This study aims to analyze the sentiment of MyTelkomsel application user reviews by applying Term Frequency–Inverse Document Frequency (TF-IDF) weighting as a feature extraction method and the Random Forest algorithm as a sentiment classification method. The research data was obtained through the process of scraping MyTelkomsel application user reviews on the Google Play Store. The research stages include data collection, text preprocessing consisting of cleaning, case folding, normalization, tokenization, stopword removal, and stemming, followed by TF-IDF weighting, sentiment labeling, classification process using Random Forest, and model evaluation using a confusion matrix, accuracy, precision, recall, and F1-score. The results showed that the Random Forest model with TF-IDF features achieved classification performance with an accuracy of 81.91%, a precision of 76.77%, a recall of 81.91%, and an F1-score of 79.11%. These results indicate that the combination of TF-IDF and Random Forest methods is capable of identifying sentiment patterns in user reviews with good performance. This study provides information on the trends in user opinions regarding the service quality of the MyTelkomsel application and can serve as evaluation material for developers in improving service quality and user experience.
Downloads
References
Pahrul, “Skripsi perbandingan metode klasifikasi random forest, xgboost dan svm pada analisis sentimen aplikasi kredit dan pinjaman online,” J. Ilk. Hasanuddin, vol. 3, no. 2, pp. 60–69, 2023.
L. Nursinggah, T. Mufizar, and U. Perjuangan, “Analisis sentimen pengguna aplikasi x terhadap program makan siang gratis dengan metode naïve bayes classifier,” J. Inform. dan Tek. Elektro Ter, vol. 12, no. 3, 2024.
T. Triyadi, R. A. Sihombing, and F. Natsir, “Perancangan Aplikasi Analisis Sentimen terhadap Opini Penghapusan Skripsi pada Twitter menggunakan Metode Naive Bayes,” Elconika J. Tek. Elektro, vol. 2, no. 1, pp. 1–14, Dec. 2023, doi: 10.33752/elconika.v2i1.5518.
M. B. Prayogi and G. Masitoh, “Analisis Sentimen Ulasan Pengguna Aplikasi Alfagift Menggunakan Random Forest,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 10, no. 2, pp. 158–170, 2025.
L. Pertiwi, “Penerapan Algoritma Text Mining, Steaming Dan Texrank Dalam Peringkasan Bahasa Inggris,” Bull. Multi-Disciplinary Sci. Appl. Technol., vol. 1, no. 3, pp. 100–104, 2022.
D. Rifaldi, Abdul Fadlil, and Herman, “Teknik Preprocessing Pada Text Mining Menggunakan Data Tweet ‘Mental Health,’” Decod. J. Pendidik. Teknol. Inf., vol. 3, no. 2, pp. 161–171, Apr. 2023, doi: 10.51454/decode.v3i2.131.
P. Machine et al., “Penerapan Machine Learning Pada Analisis Sentimen Aplikasi MyTelkomsel Menggunakan Data Ulasan Google Playstore,” Indones. J. Comput. Sci., vol. 13, no. 1, pp. 4747–4761, 2024.
A. Purwanto, D. D. H. Asih, I. Nurjabar, T. I. Cahyani, D. Hidayat, and L. Manik, “Determinant factors affecting the information system success of community-based application to fight Covid-19 pandemic,” in Proceedings of the 2021 International Conference on Computer, Control, Informatics and Its Applications, 2021, pp. 131–135.
M. D. Rizkiyanto, “Sentiment Analysis Classification on PLN Mobile Application Reviews using Random Forest Method and TF-IDF Feature Extraction-Dalam bentuk buku karya ilmiah,” J. Repos. telkomuniversity, vol. 3, no. 3, pp. 500–509, 2024.
S. A. Putri, N. Selayanti, M. Kristanaya, M. P. Azzahra, M. G. Navsih, and K. M. Hindrayani, “Penerapan Machine Learning Algoritma Random Forest Untuk Prediksi Penyakit Jantung,” in Prosiding Seminar Nasional Sains Data, 2024, pp. 895–906.
R. Aria, K. M. Lhaksmana, and R. Forest, “Klasifikasi Multi-Label Ayat-Ayat Al-Qur ’ an Menggunakan Random Forest dan Word Centrality,” J. Log., vol. 2, no. 2, pp. 9–14, 2024.
H. Muftisany, T. F. Efendi, N. Akbar, and R. Rais, “Perbandingan Kinerja Algoritma Random Forest , AdaBoost , dan Gradient Boosting dalam Memprediksi Risiko Penyakit Hipertensi,” J. Fakt. Exacta, vol. 18, no. 2, pp. 161–171, 2025.
A. Nur, A. Saputra, R. E. Saputro, and S. Saputra, “Enhancing Sentiment Analysis Accuracy Using SVM and Slang Word Normalization on YouTube Comments,” J. dan Penelit. Tek. Inform., vol. 9, no. 2, 2025, doi: 10.33395/sinkron.v9i2.14513.
A. Widodo, B. Agus Herlambang, and R. Renaldy, “Optimizing Support Vector Machine (SVM) for Sentiment Analysis of Blu by BCA Reviews with Chi-Square,” 2025.
C. D. Usman, A. P. Widodo, K. Adi, and R. Gernowo, “Rainfall prediction model in Semarang City using machine learning,” Indones. J. Electr. Eng. Comput. Sci., vol. 30, no. 2, pp. 1224–1231, 2023, doi: 10.11591/ijeecs.v30.i2.pp1224-1231.
S. Susandri, S. Defit, and M. Tajuddin, “SENTIMENT LABELING AND TEXT CLASSIFICATION MACHINE LEARNING FOR WHATSAPP GROUP,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 9, no. 1, pp. 119–125, Aug. 2023, doi: 10.33480/jitk.v9i1.4201.
N. I. Danu, “Identifikasi Berita Hoax Menggunakan Kombinasi Metode K-Nearest Neigbor (KNN) dan TF-IDF Berbasis Web Dengan Menggunakan Framework Codeigniter,” 2021, [Online]. Available: http://eprints.uwp.ac.id/id/eprint/3339/
K. Cirebon, J. Barat, and B. Indonesia, “Analisis Sentimen Ulasan Pengguna Aplikasi Kredivo Menggunakan Algoritma Support Vector Machine ( SVM ) dengan Metode TF – IDF,” vol. 2, no. 4, pp. 590–598, 2025.
T. Arya Bimantoro, Y. Rahmawati, Y. Findawati, and M. A. Rosid, “Sentiment Analysis of YouTube Comments on the East Java Grant Case Using the Support Vector Machine (SVM) Algorithm. [Analisis Sentimen Komentar Youtube Terhadap Kasus Dana Hibah Jawa Timur Menggunakan Algoritma Support Vector Machine (SVM)],” J. Abdimas, vol. 1, no. 1, pp. 1–13, 2022.
M. R. Athallah and K. M. Lhaksmana, “Hadisth Text Classification Based on Topic Using Convolutional Neural Network ( CNN ) and TF-IDF,” J. Renew. Energy, Electr. Comput. Eng., vol. 5, no. November, pp. 30–36, 2025.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Sentimen Ulasan Pengguna Aplikasi MyTelkomsel Menggunakan TF-IDF dan Random Forest
Pages: 380-392
Copyright (c) 2026 Rizki Ariyani, Masimbangan Sabrina Harlina, Masimbangan Susana Herawati
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
