Optimizing LSTM Models with FastText Feature for Sentiment Analysis of Indonesia’s 2024 Regional Elections on X - Dalam bentuk pengganti sidang - Artikel Jurnal
KHARISMA AYU
Informasi Umum
Kode
25.04.428
Klasifikasi
000 - General Works
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Data Science
Dilihat
119 kali
Informasi Lainnya
Abstraksi
The 2024 Regional Elections in Indonesia have sparked significant public discourse, generating polarized opinions as citizens actively discuss political issues, particularly on social media platforms such as X. Sentiment analysis is essential to enhance the understanding of opinion polarization reflected in these discussions. This research applies hyperparameter tuning on Long Short-Term Memory (LSTM) models enhanced with FastText feature expansion to optimize sentiment analysis accuracy for tweets about Indonesia's 2024 Regional Elections. A dataset of 60,000 tweets was collected and labeled into positive, negative, or neutral sentiments. The research involves TF-IDF feature extraction, FastText feature expansion with top similarities of 1, 5, and 10 of Tweet, Indonews, and Tweet+Indonews corpus, followed by hyperparameter tuning to optimize LSTM parameters, including number of layer, hidden size, learning rate, and epoch. The optimized LSTM models, using a top 5 similarities in the Indonews corpus, achieved
Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
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Pengarang
| Nama | KHARISMA AYU |
| Jenis | Perorangan |
| Penyunting | Erwin Budi Setiawan |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Informatika |
| Kota | Bandung |
| Tahun | 2025 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
