Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Vol. 2 No. 2 (2024): Mei: Bridge: Jurnal publikasi Sistem Informasi dan Telekomunikasi

Implementasi Model Support Vector Machine Dalam Analisa Sentimen Masyarakat Mengenai Kebijakan Penerapan Aplikasi Mypertamina

Salsabila Dwi Fitri (Universitas Jambi)
Dewi Lestari (Universitas Jambi)
Rizqa Raaiqa Bintana (Universitas Jambi)
Reni Aryani (Universitas Jambi)
Mohamad Ilhami (Universitas Jambi)
Yolla Noverina (Universitas Jambi)



Article Info

Publish Date
13 Aug 2024

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%.

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