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All Journal Jurnal Linguistik Komputasional
Mulia, Adi
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Computer Science & IT

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Analisis Sentimen dan Topik Perbincangan Netizen Indonesia Terkait Pengurangan Subsidi BBM Mulia, Adi; Dzikrillah, Akhmad Rizal
Jurnal Linguistik Komputasional Vol 7 No 1 (2024): Vol. 7, NO. 1
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v7i1.142

Abstract

Abstract- This research was conducted with the aim that is based on problems that arise in society, namely the increase in fuel prices. The sentiment classification method applied by researchers is to use a lexicon corpus dictionary that takes into account positive and negative sentiment values. The researcher then compares the sentiment between before and after the fuel price increase policy. Furthermore, the researcher applied Latent Dirichlet Allocation or (LDA) topic modeling to find out whether the discussion of the fuel price increase became the main topic when the fuel rose. The results of this study show that after announcing the fuel price increase in September 2022, the percentage of negative tweets directed at President Jokowi has increased when compared to before announcing the fuel price increase. The percentage of positive tweets directed at President Jokowi decreased when compared to before raising fuel prices. In the month when President Jokowi announced the fuel price increase policy, namely in September 2022, the topic of conversation related to the fuel price increase policy was the most popular topic of conversation in tweets directed at President Jokowi. 33.8% of tweets that discussed the fuel price increase were negative tweets with the most popular topics of discussion for netizens with negative sentiments were topics related to criticism of the Jokowi administration.
Co-Authors Dzikrillah, Akhmad Rizal