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All Journal Jurnal Pengabdian Masyarakat (JPM) Pinang Masak Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Dewi Lestari
Universitas Jambi
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Mathematics Physics

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Journal : Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi

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Implementasi Model Support Vector Machine Dalam Analisa Sentimen Masyarakat Mengenai Kebijakan Penerapan Aplikasi Mypertamina Salsabila Dwi Fitri; Dewi Lestari; Rizqa Raaiqa Bintana; Reni Aryani; Mohamad Ilhami; Yolla Noverina
Bridge : Jurnal publikasi Sistem Informasi dan Telekomunikasi Vol. 2 No. 2 (2024): Mei: Bridge: Jurnal publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i3.180

Abstract

The policy for using the MyPertamina application issued does not rule out the possibility of differences of opinion due to changes in the policy. There are many positive, neutral, and negative responses to the MyPertamina application implementation policy. To see the public's reaction to the MyPertamina application implementation policy, it can be seen through various media, including social media. Twitter is a social network that is widely used by people in Indonesia. The number of Twitter users in Indonesia reached 18.45 million in 2022, making Indonesia the fifth largest Twitter user country in the world. Researchers conducted a sentiment analysis of the search results for tweets containing the keyword "MyPertamina" using the support vector machine algorithm. 382 tweet data were obtained and classified using the support vector machine algorithm. Support vector machine is a supervised learning algorithm for data classification. SVM is very fast and effective in solving text data problems. Text data is suitable for classification with the SVM algorithm because the basic nature of text tends to be high-dimensional. Of the 382 data analyzed, the support vector machine classification using the RBF kernel with parameter C=2 gave the highest accuracy value of 80.51%, precision value of 81%, recall value of 81%, and F1 score value of 80%.
Co-Authors Arsa, Daniel Daniel Arsa Edi Saputra Khaira, Ulfa Mohamad Ilhami Pradita Eko Prasetyo Utomo Reni Aryani Reni Aryani Rizqa Raaiqa Bintana Salsabila Dwi Fitri Yolla Noverina