Chatbot Assistant for Enhancing Religious Court Services in Indonesia Using Deep Learning-based Algorithms
Keywords:
chatbot, court services, deep learning, long short-term memory, religious courtsAbstract
The Religious Courts have the authority to carry out and settle Islamic law cases and need to continue to adapt to improve legal services for Muslim communities in Indonesia. This research aims to build chatbot artificial intelligence technology to improve the services of the Religious Courts, which are optimal, available at any time, responsive, and real-time. Chatbot technology is built using the Deep Neural Network (DNN), Transfer Learning-based, and Long Short-Term Memory (LSTM) as deep learning methods. Experiments were conducted using 1,702 pairs of questions and answers with service and case labels handled by the Religious Court. Experiment results using LSTM with 50 epochs, the SoftMax activation function, and Adam's optimization show that the LSTM has an accuracy rate of 0.9583 and a loss of 0.0848. LSTM has the best results compared to Transfer Learning-based and DNN on the chatbot service of the Religious Courts in Indonesia. This research contributes to using artificial intelligence technology in the legal sector to improve the quality of Religious Courts services.
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