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All Journal Jurnas Nasional Teknologi dan Sistem Informasi CESS (Journal of Computer Engineering, System and Science) Prosiding Seminar Nasional Teknoka Jurnal Nasional Komputasi dan Teknologi Informasi Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Computer Science and Information Technology (CoSciTech)
Surya Agustian
UNIVERSITAS ISLAM NEGERI SULTAN SYARIF KASIM RIAU
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : Digital Zone: Jurnal Teknologi Informasi dan Komunikasi

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SVM Method with FastText Representation Feature for Classification of Twitter Sentiments Regarding the Covid-19 Vaccination Program Mukti M Kusairi; Surya Agustian
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 2 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v13i2.11531

Abstract

Covid-19 is a virus that has a high level of spread, making the government implement a mass vaccination program throughout Indonesia. This program received a lot of responses from the public, with positive and negative opinions or comments. Currently, the public's response through social media is also an input and consideration for the government to implement a program. Therefore, this study was conducted to produce a method approach to assessing the Covid-19 vaccination program by calculating the percentage of each sentiment class. The method used is the Support Vector Machine (SVM) and the fasttext language model feature as a representation of words in the Covid-19 vaccination sentiment dataset collected from Twitter. The data used has been dataset balancing, feature selection and parameter tuning, the optimal SVM model is obtained with a composition of 2536 training data, 778 development data and testing of 400 testing data, resulting in the best value of fi-1 score of 59% with an accuracy rate of 68%. The system is quite successful in detecting sentiment in tweets compared to before. Keywords: sentiment classification, FastText, SVM, Covid-19 vaccine.
Co-Authors Ash Shiddicky Ayu Fransiska Benny Sukma Negara Faizah Husniah Fitri Insani Fuji Astuti Habib Hakim Sinaga Halimah Iis Afrianty Muhammad Fikry Muhammad Irsyad Mukti M Kusairi Pizaini Pizaini Rahmad Kurniawan Reski Mai Candra Siti Ramadhani Trya Ayu Pratiwi