<![CDATA[Every year, the Faculty of Engineering at Nurul Jadid University forms a committee to manage the process of students' final projects from the title selection stage to the final examination process until graduation. The process of selecting the final project title is still done manually, namely by checking the titles one by one, which takes a long time and allows errors because there is a lot of data to check, so human errors can also occur. Therefore, this research proposes to use the Bidirectional Long Short Term Memory (BiLSTM) method to classify the final project title based on its grade category. Several experiments were conducted to generate the most appropriate labels. The first experiment produced 4 labels and the second experiment produced 2 labels. From the results of several experiments, it was concluded that the second experiment had the best accuracy results with the 'good enough' and 'good' classes. The oversampling technique was then applied to overcome overlapping data, and the turning process was then performed on several parameters that could re-optimize the previous accuracy result of 75.24% to 91.15%. With a configuration of 10 random state parameters, using 64 batch sizes and 50 epochs. In addition, model adjustments were made to the hidden layer by adding a dropout layer and relu activation.]]>
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