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Journal : Journal of Computer Networks, Architecture and High Performance Computing

Sentiment Analysis of Starlink on Twitter Using Support Vector Machine Algorithm Sardin, Sardin; Nugroho, Agung; Kurniadi, Nanang Tedi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4348

Abstract

Indonesia faces unique challenges in the provision of internet services. Cable and fiber optic infrastructure is often difficult and expensive to implement in many areas. Based on data from the Asosiasi Penyelenggara Jasa Internet Indonesia (APJII), by 2024 internet users will reach 221.5 million. Starlink, Elon Musk's satellite-based internet service through his SpaceX company, offers an innovative solution to fill the void in Indonesia's telecommunications infrastructure. However, Starlink's presence has raised concerns among local service providers, particularly regarding potential market disruption and existing regulations. Starlink could also pose a potential threat to Indonesia's security and sovereignty. Starlink has been a hot topic on various social media platforms, including Twitter. Twitter is a very popular social media platform with millions of active users who often share their opinions in real-time. The number of public responses in assessing the presence of Starlink in Indonesia, became a reference for a sentiment analysis. The Support Vector Machine algorithm is used to classify opinions into positive and negative categories. Based on testing that has been done using the Cross Validation technique with a K-Fold value with a total of 1976 tweets data. The results show that 1112 tweets contain positive sentiment and 864 tweets contain negative sentiment. This shows that 56.3% of people agree with the presence of starlink in Indonesia. While from the use of the Support Vertor Machine algorithm, the Accruracy value is 76.22%, Precision is 77.48%, and Recall is 81.38%.