<![CDATA[In general, classification is defined as a learning method that classifies data into class labels. It can be perfomed on both structured and unstructured data, based on the data training that has been done. This research leverages Bidirectional LSTM technology in order to develop a news classification model using the CBOW architectures using Word2vec as a word vector. In this research, three main parameters are used: embedding size, window size, and units bilstm. The effects of these three parameters will show optimization of model performance. The results of the constructed model are measured using the accuracy, recall, precision, f1-score and computational time metrics. The findings revealed the greatest performance for title data was for the model with windows size 3, embedding size 200 and unit 128 with 79,18% accuracy. Meanwhile, the data content model has the best performance, on windows size 5, embedding size 300 and units 256 with 92,80% accuracy.]]>
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