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All Journal Unnes Journal of Sport Sciences JISIP: Jurnal Ilmu Sosial dan Pendidikan Budimas : Jurnal Pengabdian Masyarakat jurnal ilmu keolahragaan Jurnal Teknik Informatika (JUTIF) Jurnal Poster Pirata Syandana PIPER Algoritme Jurnal Mahasiswa Teknik Informatika Prosiding University Research Colloquium West Science Business and Management TECHNOLOGIC
Khoerul Anam, Khoerul
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Journal : Jurnal Teknik Informatika (JUTIF)

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Comparison ff Sentiment Labeling Using Textblob, Vader, and Flair in Public Opinion Analysis Post-2024 Presidential Inauguration with IndoBERT Kusnawi, Kusnawi; Anam, Khoerul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4015

Abstract

The results of the 2024 Indonesian presidential election decided that Prabowo Subianto and Gibran Rakabuming Raka became the elected pair of Indonesian presidential and vice-presidential candidates in 2024. The pair's election triggered various public reactions, especially on social media platforms. Some social media platforms provided diverse opinions, indicating a wide variety of views on this issue. This research aims to analyze public opinion after the election of the 2024 Indonesian president by comparing sentiment using TextBlob, VADER (Valence Aware Dictionary and sEntiment Reasoner), and Flair. Training and testing are done with the IndoBERT model to determine the most effective sentiment labeling. This research starts by collecting text data from social media X, YouTube, and Instagram, then preprocessing, translating, and labeling data using three libraries, training, and testing using IndoBERT. The results of training and testing data show that Flair has an accuracy of 81.29%, TextBlob has an accuracy of 73.35%, and VADER has an accuracy of 74.86%. From the accuracy results obtained, it can be concluded that labeling using Flair provides the greatest accuracy of the others because the Flair labeling process uses deep learning and contextual embedding techniques.
Co-Authors A T manurung, Vuko Al Basyari, M. Mahbub Anggreini, Silvia Diah Astutiningrum E Ernawati, E Eka Riyanti Fachri, Fahmi Fikri, Izzul Herniyatun, H Idham Kholid Kusnawi Kusnawi Lestari, Linda M. Slamet Yahya, M. Slamet Mikkey Anggara Suganda Nasuka Nasuka, Nasuka Rosyidi, Ahmad Anwar Sri Sumarni Tri Aji Utami, Aprilia Wilis , Ratih Anggoro Yusuf Setyadi