Debiasing Pemberitaan Online terhadap Berita Pemilu Presiden di Indonesia

Yuniarti, Lidiya (2024) Debiasing Pemberitaan Online terhadap Berita Pemilu Presiden di Indonesia. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pemberitaan politik mengenai presiden dalam media massa termasuk berita online yang memiliki peran penting dalam proses pemilu dan dalam membentuk opini publik. Namun, pemberitaan tersebut cenderung rentang terhadap bias (seperti nama kandidiat, partai politik dan organisasi yang terlibat dalam pemilu) sehingga dapat memengaruhi persepsi dan partisipasi politik dari masyarakat. oleh karena itu, penting untuk mengindentifikasi dan mengatasi bias dalam pemberitaan politik khususnya terkait pemilu presiden di Indonesia. Adapun metodologi yang digunakan dalam penelitian ini menggunakan word embedding yaitu model Word2Vec dan IndoBERT dalam menganalisis resepsentasi dalam pemberitaaan pada berita politik khususnya pemilu presiden di Indonesia dan menggunakan sentimen analisis berbasis leksikon untuk memastikan proses debiasing yang diterapkan berhasil mengurangi bias. Adapun hasil dari penelitian ini representasi bias menggunakan word embedding berhasil dilakukan, namun metode sentimen analisis berbasis leksikon yang digunakan tidak efektif dalam melakukan melakukan evaluasi bias pada berita pemilu presiden 2024 di Indonesia dibuktikan dengan naiknya nilai sentimen yang menunjukkan kecenederungan sentimen dalam analisis seperti naiknya sentimen negatif sebesar 0,18 dan sentimen positif sebesar 2,05 serta terjadi penurunan nilai sentimen netral sebesar 2,24 setelah dilakukan proses debiasing.
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Political news coverage regarding the president in mass media, including online news, plays an important role in the election process and in shaping public opinion. However, such coverage tends to be susceptible to bias (such as the names of candidates, political parties, and organizations involved in the election), which can influence public perception and political participation. Therefore, it is important to identify and address bias in political news coverage, especially related to the presidential election in Indonesia. The methodology used in this study involves word embeddings, specifically the Word2Vec and IndoBERT models, to analyze representations in political news, particularly the Indonesian presidential election. Additionally, a lexicon-based sentiment analysis is used to ensure that the debiasing process effectively reduces bias. The findings of this study indicate that bias representation using word embeddings was successfully conducted. However, the lexicon-based sentiment analysis method used was not effective in evaluating bias in the news of the 2024 Indonesian presidential election. This is evidenced by the increase in sentiment values, indicating a tendency in sentiment analysis, with a rise in negative sentiment by 0.18, positive sentiment by 2.05, and a decrease in neutral sentiment by 2.24 after the debiasing process.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Debiasing, isu calon presiden, word embedding, sentimen analisis, Debiasing, presidential candidate issues, sentiment analysis
Subjects:	Z Bibliography. Library Science. Information Resources > ZA Information resources > Z699.5 Information storage and retrieval systems
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information System > 59101-(S2) Master Thesis
Depositing User:	Lidiya Yuniarti
Date Deposited:	07 Aug 2024 22:51
Last Modified:	29 Aug 2024 08:36
URI:	http://repository.its.ac.id/id/eprint/113217

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