Comparative Analysis of LSTM and Bi-LSTM for Classifying Indonesian New Translation Bible Texts Using Word2Vec Embedding
The research aims to compare the classification accuracy of Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) architectures in classifying Indonesian New Translation Bible texts with Word2Vec embedding. The main objective is to examine how these deep learning models addressed complex a...
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| Asıl Yazarlar: | , , , |
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| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
Bina Nusantara University
2025-09-01
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| Seri Bilgileri: | CommIT Journal |
| Konular: | |
| Online Erişim: | https://journal.binus.ac.id/index.php/commit/article/view/12015 |
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