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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Detalles Bibliográficos

Principais autores:	Marthen Sattu Sambo, Suryasatriya Trihandaru, Didit Budi Nugroho, Hanna Arini Parhusip
Formato:	Artigo
Idioma:	Inglês
Publicado:	Bina Nusantara University 2025-09-01
Series:	CommIT Journal
Assuntos:	Text Classification Long Short Term Memory (LSTM) Bidirectional-Long Short Term Memory (Bi-LSTM) Indonesian New Translation Bible Word2Vec Embedding
Acceso en liña:	https://journal.binus.ac.id/index.php/commit/article/view/12015
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