Abstract:
The Jambi Regional Police Department (Polda Jambi) has introduced an innovative system for managing public complaints
via WhatsApp. However, this implementation faces challenges due to the manual documentation process, leading to poorly documented
and inaccurate complaints. To address these issues, this study proposes the development of a text mining-based system utilizing deep
learning to facilitate the accurate categorization of public complaints, thereby streamlining the police's processing of these reports.
Deep learning, specifically through Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM),
offers a robust framework for learning patterns from complaint data. This study develops and optimizes a CNN-BiLSTM architecture,
involving the adjustment of layer configurations and the application of early stopping techniques to prevent overfitting. The proposed
architecture is tested on two datasets: public complaints submitted to the Indonesian National Police via WhatsApp and Tweets from
social media X. Experimental results indicate high performance across both datasets, with the architecture achieving a peak accuracy
of 99% on the police data and 79% on the Twitter data. The highest-performing model is then integrated into a graphical user interface
(GUI) using Streamlit, enabling the efficient and accurate classification of public complaints. This system demonstrates significant
potential for enhancing the efficiency and accuracy of complaint management processes within law enforcement agencies. The findings
suggest that integrating advanced deep learning techniques into public complaint systems can substantially improve the documentation
and categorization of complaints, providing a scalable solution for law enforcement operations.
