Virtual Assistant for Local Government Units Using BERT-Based Natural Language Processing

Local Government Units (LGUs) play a critical role in public service delivery, yet they often encounter challenges such as limited resources, bureaucratic inefficiencies, and high volumes of citizen inquiries. To address these issues, this paper proposes the development of a virtual assistant powered by Natural Language Processing (NLP), specifically utilizing the Bidirectional Encoder Representations from Transformers (BERT) model. This assistant is designed to automate the handling of common citizen inquiries, thereby enhancing the efficiency of service delivery. The virtual assistant was implemented as a web application using the Django framework, with an evaluation based on ISO 9126 standards, including aspects of functionality, reliability, usability, and efficiency. The findings demonstrate that the BERT-based NLP system significantly improves the responsiveness and accuracy of LGU services, leading to enhanced citizen satisfaction and more streamlined public service operations. The model achieved an Exact Match score of 48.0 and an F1 score of 68.01, indicating its effectiveness in accurately understanding and responding to citizen queries. Additionally, the system scored highly in usability, efficiency, maintainability, and portability, confirming its ease of use and operational effectiveness. These results give rather strong evidence that the use of the NLP-driven virtual assistant can be beneficial for increasing the citizen satisfaction and improving the effectiveness of the public service delivery.