Penerapan Support Vector Machine untuk Analisis Sentimen Pengguna X terhadap IndiHome, Biznet, dan Starlink
-
Zhevin Alfian
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- M Afdal Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Rice Novita Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Zarnelly Zarnelly Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
This study aims to analyze user sentiment on the social media platform X toward three major internet service providers in Indonesia, IndiHome, Biznet, and Starlink. The analysis focuses on five key variables: internet speed, network stability, pricing and service packages, customer service quality, and coverage availability. A total of 4,500 data points were collected through data crawling, then processed using text mining techniques and the Support Vector Machine (SVM) algorithm, with data imbalance addressed through the Random Oversampling method. Evaluation results show that IndiHome consistently demonstrated the best performance, achieving an accuracy of up to 90% in the customer service quality variable, and an overall average accuracy above 85% across all variables. Biznet generally ranked second, with accuracy ranging from 63% to 80%. Starlink placed lowest overall, although it still recorded competitive results, such as 82% accuracy in the internet speed variable. The application of Random Oversampling improved the model’s classification accuracy by an average of 6–12% compared to the non-oversampling model. This study offers strategic insights into public perception of internet services and can serve as a reference for improving service quality based on data-driven user feedback.
Downloads
References
M. F. Fachrudin, C. V. Angkoso, and D. A. Fatah, “Analisis Sentimen Pada Sosial Media Twitter Terhadap Kualitas Jaringan Internet Telkomsel Menggunakan Ensemble K-Nearest Neighbour -Support Vector Machine,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 6, pp. 1253–1264, Dec. 2024, doi: 10.25126/jtiik.2024118713.
M. H. A. Sunata, F. Irwiensyah, and F. N. Hasan, “Analisis Sentimen Calon Presiden 2024 di Media Sosial X Menggunakan Naive Bayes dan SMOTE,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 8, no. 3, p. 1313, Jul. 2024, doi: 10.30865/mib.v8i3.7708.
Diana Puspitasari and Tata Sutabri, “Analisis Sentimen Berdasarkan pada Twitter (X) terhadap Layanan Indihome Menggunakan Algoritma Support Vector Machine (SVM),” JUMINTAL: Jurnal Manajemen Informatika dan Bisnis Digital, vol. 3, no. 2, pp. 58–71, Nov. 2024, doi: 10.55123/jumintal.v3i2.4449.
H. J. Christanto, “Game Theory Analysis of Indihome and Biznet in the Salatiga Internet Market,” Journal of Information Systems and Informatics, vol. 6, no. 1, pp. 399–408, Mar. 2024, doi: 10.51519/journalisi.v6i1.677.
B. W. Sari and F. F. Haranto, “Implementasi Support Vector Machine untuk Analisis Sentimen Pengguna Twitter Terhadap Pelayanan Telkom dan Biznet,” Jurnal Pilar Nusa Mandiri, vol. 15, no. 2, pp. 171–176, Sep. 2019, doi: 10.33480/pilar.v15i2.699.
J. Garcia, M. Beckerle, S. Sundberg, and A. Brunstrom, “Modeling and predicting starlink throughput with fine-grained burst characterization,” Comput Commun, vol. 234, Mar. 2025, doi: 10.1016/j.comcom.2025.108090.
M. Ridwan, T. Al Islami, Daffa, M., P. Rahayu, F. Az Zahra, R., and A. Naerul, Edwin, K., “Oligopoli Telekomunikasi dan Inovasi : Analisis Dampak Masuknya STARLINK bagi Industri Telekomunikasi di Indonesia,” Kampus Akademik Publising Jurnal Ilmiah Ekonomi Dan Manajemen, vol. 2, no. 12, pp. 306–312, 2024, doi: 10.61722/jiem.v2i12.3206.
J. Pebrianto, “Sentiment Analysis Of Service Provider On Twitter Tweet Using Naive Bayes Classifier With PHP,” Journal of Innovation And Future Technology (IFTECH), vol. 5, no. 2, pp. 13–23, 2023, doi: 10.47080/iftech.v5i2.2752.
A. Nugroho and N. Tedi Kurniadi, “Sentiment Analysis of Starlink on Twitter Using Support Vector Machine Algorithm,” Architecture and High Performance Computing, vol. 6, no. 3, 2024, doi: 10.47709/cnapc.v6i3.4348.
M. Khalil Gibran et al., “Sentiment Analysis of Platform X Users on Starlink Using Naive Bayes,” Instal: Jurnal Komputer, no. 03, pp. 210–220, 2024, doi: 10.54209/jurnalinstall.v16i03.240.
H. Jia and S. Shen, “Benders Cut Classification via Support Vector Machines for Solving Two-Stage Stochastic Programs,” INFORMS Journal on Optimization, vol. 3, no. 3, pp. 278–297, 2021, doi: 10.1287/ijoo.2019.0050.
M. Nur Akbar and N. Annisa Safitri Yusuf, “Analisis Sentimen Pengguna Indihome dengan Metode Klasifikasi Support Vector Machine (SVM),” Journal Shift, vol. 2, no. 1, pp. 14–21, 2022, doi: 10.24252/shift.v2i1.18.
A. R. Muhammad Fikri, J. Jondri, and W. Astuti, “Sentiment Analysis Against IndiHome and First Media Internet Providers Using Ensemble Stacking Method,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 2, Sep. 2022, doi: 10.47065/bits.v4i2.1969.
J. Lu, “Data Analytics Research-Informed Teaching in a Digital Technologies Curriculum,” vol. 20, no. 2, pp. 57–123, 2020, doi: 10.1287/ited.2019.0215.
A. Addiga and S. Bagui, “Sentiment Analysis on Twitter Data Using Term Frequency-Inverse Document Frequency,” Journal of Computer and Communications, vol. 10, no. 08, pp. 117–128, 2022, doi: 10.4236/jcc.2022.108008.
F. Hashfi, D. Sugiarto, and I. Mardianto, “Sentiment Analysis of An Internet Provider Company Based on Twitter Using Support Vector Machine and Naïve Bayes Method,” Ultimatics : Jurnal Teknik Informatika, vol. 14, no. 1, pp. 1–6, 2022, doi: 10.31937/ti.v14i1.2384.
R. Muliani, A. Solehudin, and A. Jamaludin, “Analisis Sentimen Terhadap Provider XYZ di Twitter Menggunakan Algoritma Naive Bayes Classifier,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 4, pp. 2841–2848, Aug. 2024, doi: 10.36040/jati.v7i4.7191.
N. Chamidah, D. Widiyanto, H. B. Seta, and A. A. Aziz, “The Impact of Oversampling and Undersampling on Aspect-Based Sentiment Analysis of Indramayu Tourism Using Logistic Regression,” Revue d’Intelligence Artificielle, vol. 38, no. 3, pp. 795–804, Jun. 2024, doi: 10.18280/ria.380306.
A. Miftahusalam, A. F. Nuraini, A. A. Khoirunisa, and H. Pratiwi, “Comparison of Random Forest, Naïve Bayes, and Support Vector Machine Algorithms in Analyzing Twitter Sentiment Regarding Public Opinion on the Removal of Honorary Employees,” Seminar Nasional Official Statistics, vol. 2022, no. 1, pp. 563–572, 2022, doi: https://doi.org/10.34123/semnasoffstat.v2022i1.1410.
P. M. Susanti, M. Afdal, I. Permana, and A. Marsal, “Klasifikasi Sentimen Pengguna X Terhadap Pemboikotan Produk Pro Israel Menggunakan Algoritma Machine Learning,” Technology and Science (BITS), vol. 6, no. 4, 2025, doi: 10.47065/bits.v6i4.6533.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan Support Vector Machine untuk Analisis Sentimen Pengguna X terhadap IndiHome, Biznet, dan Starlink
Pages: 929-938
Copyright (c) 2025 Zhevin Alfian, M Afdal, Rice Novita, Zarnelly Zarnelly
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).
