THE PREDICTION OF RETWEET USING LONG SHORT-TERM MEMORY METHOD WITH THE TOPIC OF COVID-19 VACCINATION
ZAHRA FADIAH PUTRI
Informasi Umum
Kode
23.04.2598
Klasifikasi
006.35 - Natural Language Processing, Computer Science
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Natural Language Processing, Memory And Learning,
Dilihat
212 kali
Informasi Lainnya
Abstraksi
<p style="margin-left:34.1pt;"><em>In last 2019, the Covid-19 outbreak was first reported, infecting at least 20.1 million people and killing more than 737,000 people worldwide and still counting. In Indonesia, the government announced that the Covid-19 vaccination is an obligation for everyone. The rapid development of technology has made social media a platform of spreading news. One of the social media that plays an important role is Twitter. The tweets can be shared with other users by re-tweeting process.The greater the number of retweets, the wider the existing information. Therefore, the retweet feature plays a crucial role in spreading information. This study discusses retweet predictions about Covid-19 vaccination using the Long Short-Term Memory (LSTM) method with the application of hyperparameter tuning to obtain the best result and get an accuracy or closeness values (98%),a precision or closeness value (74%), a recall value (91%), and an f-1 score value or a comparison value (93%).</em></p>
Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
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Pengarang
| Nama | ZAHRA FADIAH PUTRI |
| Jenis | Perorangan |
| Penyunting | Jondri, Indwiarti |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Informatika |
| Kota | Bandung |
| Tahun | 2023 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
