<![CDATA[The SiBayar website is being developed by the Sabilurrosyad Islamic Boarding School to facilitate its administration and management. To improve the functionality of the website, user feedback is needed on the existing features. However, managing and analyzing a large amount of user feedback manually can be a very time-consuming process. Therefore, an automated approach such as text summarization is needed to summarize and analyze the data. This study aims to generate an automated summary of user feedback on the SiBayar website of the Sabilurrosyad Islamic Boarding School using the Bi-Directional Long Short-Term Memory (Bi-LSTM) method, focusing on identifying the best parameters through hyperparameter tuning and evaluating the accuracy in full. The results of the hyperparameter tuning test show that the configuration that provides the best performance is the one using the Nadam algorithm optimization, the number of layers 1 and batch size 1, and the variational dropout with a dropout rate of 0.5. The model summary quality evaluation was performed using the ROUGE metric which showed that the Bi-LSTM model achieved a ROUGE-1 score of 0.6221, a ROUGE-2 score of 0.5462, and a ROUGE-L score of 0.660. Overall, Bi-LSTM model in this study has good performance in summarizing text, but the suitability of word pairs and sequences still needs to be improved for more optimal results.]]>
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