SENTIMENT ANALYSIS OF THE CYANIDE COFFEE CASE USING LSTM, CNN, AND WORD2VEC METHODS - Dalam bentuk buku karya ilmiah
MOCHAMMAD NABIEL R RAMADHAN
Informasi Umum
Kode
25.04.1399
Klasifikasi
000 - General Works
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Natural Language Processing (nlp)
Dilihat
55 kali
Informasi Lainnya
Abstraksi
The "Cyanide Coffee" case has attracted public attention because it involved the death of Mirna Salihin after consuming coffee containing cyanide. This study conducted a sentiment analysis public comments related to this case on social media Youtube using the LSTM and CNN algorithms with the Word2Vec extraction feature. Dataset used con-sisted 4,361 comments in Indonesian classified into two categories: positive and negative. The results showed that the LSTM model with Word2Vec produced an accuracy 81,40%, a precision 81,73%, a recall 90,44%, and an F1-score 85,86%, outperforming CNN model which achieved an accuracy 79,15% for the positive and negative categories. These findings prove the superiority of LSTM in handling sequential data contexts compared to CNN, especially in text-based sentiment analysis tasks. This study makes a significant contribution to under-standing public opinion on legal cases through a deep learning ap-proach, as well as demonstrating the effectiveness of the Word2Vec feature in improving the performance of sentiment analysis models.
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Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
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Pengarang
| Nama | MOCHAMMAD NABIEL R RAMADHAN |
| Jenis | Perorangan |
| Penyunting | Kemas Muslim Lhaksmana, Widi Astuti |
| 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 |
