Hadith Text Classification Based on Topic Using Convolutional Neural Network (CNN) and TF-IDF - Dalam bentuk pengganti sidang - Artikel Jurnal
M RAFI ATHALLAH
Informasi Umum
Kode
25.04.1367
Klasifikasi
000 - General Works
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Data Science
Dilihat
105 kali
Informasi Lainnya
Abstraksi
Convolutional Neural Networks (CNN) will develop a hadith classification system to categorize texts based on specific topics or categories. This study compares two text representation techniques, namely Term Frequency- Inverse Document Frequency (TF-IDF) and Word2Vec, concerning the application of stemming and without stemming in the process. This study utilizes Category ID 0-5. About 2,845 data have been processed as required for testing. The data was divided into two parts, with a proportion of 80:20 for training and testing. Next, several models were evaluated, namely Word2Vec with stemming, TFIDFCNN without stemming, and TFIDFCNN with stemming. Accuracy, precision, recall, and F1 score metrics were used to assess the performance. The results show that the TFIDFCNN model without stemming performs best with 85% accuracy in topic-based text classification. This is due to the stability and efficiency of the model in processing data.
Koleksi & Sirkulasi
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Pengarang
| Nama | M RAFI ATHALLAH |
| Jenis | Perorangan |
| Penyunting | Kemas Muslim Lhaksmana |
| 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 |
