PRADANA, BAGASKARA NUR (2024) RESPON PUBLIK TERHADAP NAIK DAN TURUNNYA HARGA PENERBANGAN DOMESTIK DAN INTERNASIONAL DI 2022 MENGGUNAKAN PENDEKATAN MACHINE LEARNING. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Social media, particularly Twitter, is currently experiencing rapid growth as a conversational platform covering various topics such as business, politics, society, culture, and law. In this context, the research is focused on the fluctuations in flight ticket prices in 2022. Although this topic generates enthusiasm, sharp debates emerge on Twitter, reflecting diverse perspectives and controversies among users. Sentiment analysis, an automated process of mining and processing unstructured text data, is proposed to be applied to tweets related to changes in flight ticket prices in Indonesia. In its application, machine learning methods such as Naïve Bayes and Long Short-Term Memory (LSTM) are suggested to classify sentiments into three main categories: positive, negative, and neutral. Testing is conducted on 14,168 tweets related to changes in flight ticket prices on the Twitter social media platform, yielding a profound understanding of the sentiments and public perspectives on the subject. Keywords: twitter, flight ticket, sentiment analysis, naïve bayes, lstm Media sosial, khususnya Twitter, tengah mengalami pertumbuhan pesat sebagai platform pembicaraan yang mencakup berbagai topik seperti bisnis, politik, masyarakat, budaya, dan hukum. Dalam konteks ini, penelitian fokus pada Naik Turunnya harga tiket penerbangan di tahun 2022. Meskipun topik ini menciptakan antusiasme, perdebatan yang tajam muncul di Twitter, mencerminkan beragam sudut pandang dan kontroversi di antara pengguna. Analisis sentimen, suatu proses yang secara otomatis menggali dan mengolah data teks tidak terstruktur, diusulkan untuk diterapkan pada tweet terkait perubahan harga tiket penerbangan di Indonesia. Dalam penerapannya, metode machine learning, seperti Naïve Bayes dan Long Short-Term Memory (LSTM), diajukan untuk mengklasifikasikan sentimen dalam tiga kategori utama: positif, negatif, dan netral. Pengujian dilakukan pada 14.168 tweet terkait perubahan harga tiket penerbangan di media sosial Twitter, menghasilkan pemahaman mendalam tentang suasana hati dan pandangan masyarakat terhadap topik tersebut. Kata kunci: twitter, tiket pesawat, analisis sentimen, naïve bayes, lstm
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