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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika)
Efrian Maryansyah
Universitas Nusa Mandiri
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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Journal : J-SAKTI (Jurnal Sains Komputer dan Informatika)

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Analisis Sentimen Pada Twitter @Ovo_Id dengan Metode Support Vectore Machine (SVM) Hendri Sulastomo; R Ramadiansyah; Khalil Gibran; Efrian Maryansyah; Aththoriqh Tegar
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.514

Abstract

Social networking helps internet users communicate. This is because social network users can convey messages by utilizing the facilities prepared by each social media. Social media users' messages can be used in various ways, such as a review of a product or a review of a problem in politics or current social problems. This can be done by analyzing the sentiments of social media users. The support vectore machine method is one method that can be used to analyze sentiment. In sentiment analysis using the support vectore machine method, it is done by classifying sentiment into compliant or not compliant classes. The accuracy rate of sentiment analysis for @Ovo_ID using the support vectore machine method is 94% using 1000 tweet data.
Co-Authors Aththoriqh Tegar Hendri Sulastomo Khalil Gibran R Ramadiansyah