Analisis Sentimen Terhadap Kualitas Perbandingan Satelit Starlink dan Telkom Menggunakan Pendekatan Machine Learning

Gianina Dea Aneria; Alz Danny Wowor

doi:10.31004/innovative.v5i4.21085

Authors

Gianina Dea Aneria Universitas Kristen Satya Wacana
Alz Danny Wowor Universitas Kristen Satya Wacana

DOI:

https://doi.org/10.31004/innovative.v5i4.21085

Keywords:

machine learning, sentiment analysis, service quality, Starlink, Telkomsel

Abstract

The development of satellite communication technology has created opportunities for high-speed internet services across various regions, including remote areas. Starlink, which operates using low Earth orbit (LEO) satellites, and Telkom, which utilizes geostationary (GEO) satellites alongside terrestrial infrastructure, are two major providers in Indonesia. This study examines sentiment analysis of the service quality of Starlink and Telkom using a machine learning approach. Data were collected from the X (Twitter) platform and processed through cleaning, case folding, tokenization, slang word normalization, stopword removal, and stemming. Three classification algorithms were employed: Naïve Bayes, Decision Tree, and Random Forest. The results show that Decision Tree achieved the highest accuracy of 100%, followed by Random Forest at 96% and Naïve Bayes at 80%. Sentiment analysis revealed that Starlink was favored for its speed and connection stability, whereas Telkom was more appreciated for its affordability and service coverage. These findings provide an objective overview of user perceptions that can be utilized to improve service quality and strategic planning.

References

Anugrah, A., Hermanto, T. I., & Kaniawulan, I. (2022). Sentiment Analysis of Internet Service Providers Using Naïve Bayes Based on Particle Swarm Optimization. Jurnal Riset Informatika, 4(4), 371–378. https://doi.org/10.34288/jri.v4i4.408

Fauzan, F. J., M Afdal, Rice Novita, & Mustakim. (2024). Penerapan Machine Learning Pada Analisis Sentimen Aplikasi Mytelkomsel Mengunakan Data Ulasan Google Playstore. Indonesian Journal of Computer Science, 13(3), 4747–4761. https://doi.org/10.33022/ijcs.v13i3.4024

Israt Jahan, Md Nakibul Islam, Md Mahadi Hasan, & Md Rafiuddin Siddiky. (2024). Comparative analysis of machine learning algorithms for sentiment classification in social media text. World Journal of Advanced Research and Reviews, 23(3), 2842–2852. https://doi.org/10.30574/wjarr.2024.23.3.2983

Issue, V., & Hukunala, S. V. (2024). Authentica PROVIDERS IN INDONESIA : A BUSINESS LAW REVIEW. 7(1), 1–9. https://doi.org/10.20884/1.atc.2024.7.1.455

Khaliq, M. Y., Rakib, M., & Aris, V. (2024). Sentiment Analysis to Starlink Services in Indonesia. International Journal Of Latest Technology In Engineering & Management (IJLTEM), 9(6), 13–21.

Kumar, S., Roy, P. P., Dogra, D. P., & Kim, B.-G. (2023). A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications. 1–29. http://arxiv.org/abs/2311.11250

Mohan, N., Ferguson, A. E., Cech, H., Bose, R., Renatin, P. R., Marina, M. K., & Ott, J. (2024). A Multifaceted Look at Starlink Performance. WWW 2024 - Proceedings of the ACM Web Conference, 2024(2), 2723–2734. https://doi.org/10.1145/3589334.3645328

Nagar, M., Kumar, C., & Puri, S. (2025). Optimizing Sentiment Analysis with Naïve Bayes and Random Forest Techniques: A Result-Based Approach. Journal of Artificial Intelligence Research & Advances, 12(2), 46–57. https://doi.org/10.37591/joaira.v12i02.204465

Sugiono, M. (2019). Instal: Jurnal Komputer. 15(June), 113–119. https://scholar.archive.org/work/te4arwrevjhhdg47urjqwoha2y/access/wayback/https://journalinstal.cattleyadf.org/index.php/Instal/article/download/8/8

Surbakti, F. P. S., & Ginting, A. A. (2023). The Application of Sentiment Analysis on Customers Responses in Social Media (Case Study: Indihome and First Media Internet Service Provider). Jurnal METRIS, 23(02), 68–73. https://doi.org/10.25170/metris.v23i02.4150

Syah Putra, F. M., Rakasiwi, S., & Ariyanto, N. (2025). Twitter Sentiment Classification towards Telecommunication Provider Users in Indonesia. Journal of Applied Informatics and Computing, 9(2), 314–321. https://doi.org/10.30871/jaic.v9i2.9143

Syahrohim, I., Saputra, S. D., Saputra, R. W., Pranatawijaya, V. H., & Priskila, R. (2024). Perbandingan Analisis Sentimen Setelah Pilpres 2024 Di Twitter Menggunakan Algoritma Machine Learning. Jurnal Informatika Dan Teknik Elektro Terapan, 12(2). https://doi.org/10.23960/jitet.v12i2.4249

Taofiqurrohman, H., & Roji, F. F. (2025). Telkom Stock Price Prediction Using Prophet : Analysis of the Effect of Public Sentiment on the Presence of Starlink Prediksi Harga Saham Telkom Menggunakan Prophet : Analisis Pengaruh Sentimen Publik Terhadap Kehadiran Starlink. 5(April), 484–495.