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All Journal Jurnal Informatika Universitas Pamulang
Ramadanu Putra
Universitas Islam Negeri Sultan Syarif Kasim Riau
Computer Science & IT

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Penerapan Metode SVM pada Klasifikasi Sentimen terhadap Anies Baswedan sebagai Bakal Calon Presiden 2024 Ramadanu Putra; Yusra Yusra; Muhammad Fikry
Jurnal Informatika Universitas Pamulang Vol 8, No 2 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i2.30355

Abstract

Twitter is one of the most popular and rapidly growing platforms. Through Twitter, users can write and share various activities and opinions, including opinions about 2024 presidential candidates. Several candidates who are suitable to replace the president of Indonesia in 2024 have become the talk of the news media. Anies Baswedan is one of the presidential candidates who has been proposed by the National Democratic Party (NasDem) on October 3, 2022. The opinions of Twitter users can be seen through tweets about Anies Baswedan as a 2024 presidential candidate. These tweets can be analyzed to obtain information on public sentiment towards Anies Baswedan as a 2024 presidential candidate. Therefore, this study aims to apply the Support Vector Machine method in classifying sentiment towards Anies Baswedan as a 2024 presidential candidate. The dataset amounted to 3400 with positive labels as many as 2130 tweets and negative labels as many as 1270 tweets. Labeling is done manually with crowdsourced labelling techniques, obtained a kappa value of 0.68 which shows the level of agreement is relatively strong. Text preprocessing process is carried out. The dataset is divided into training data and test data with a ratio of 90:10. The SVM model with RBF kernel using C=9 and γ=2 parameter pairs has successfully produced good results in validation and evaluation. The accuracy results obtained were 90.61%, precision of 90.67%, recall of 90.61% and f1-score of 90.61%.
Co-Authors Muhammad Fikry Yusra, Yusra