Research Article
Development of a Natural Language Processing-Based Virtual Assistant for Optimizing Academic Services
@INPROCEEDINGS{10.4108/eai.16-9-2025.2361164, author={Fahmy Syahputra and Harvei Desmon Hutahaean and Amirhud Dalimunthe and Saras Pratama}, title={Development of a Natural Language Processing-Based Virtual Assistant for Optimizing Academic Services}, proceedings={Proceedings of the 7th International Conference on Innovation in Education, Science, and Culture, ICIESC 2025, 16 September 2025, Medan, Indonesia}, publisher={EAI}, proceedings_a={ICIESC}, year={2026}, month={3}, keywords={virtual assistant natural language processing academic services artificial intelligence educational technology}, doi={10.4108/eai.16-9-2025.2361164} }- Fahmy Syahputra
Harvei Desmon Hutahaean
Amirhud Dalimunthe
Saras Pratama
Year: 2026
Development of a Natural Language Processing-Based Virtual Assistant for Optimizing Academic Services
ICIESC
EAI
DOI: 10.4108/eai.16-9-2025.2361164
Abstract
The rapid advancement of technology in the field of artificial intelligence has encouraged the development of innovative solutions to improve service quality in higher education. This research focuses on the development of a virtual assistant based on Natural Language Processing (NLP) to optimize academic services. The virtual assistant is designed to facilitate various administrative and academic processes such as course registration, academic information retrieval, and student consultation scheduling. The system integrates NLP techniques for intent recognition, entity extraction, and context management, enabling it to understand and respond to user queries accurately. The development method used follows the agile model with iterative testing to ensure system reliability and user-friendliness. Evaluation results involving 100 students showed a 92% success rate in correctly understanding user queries and a significant reduction in service response time. The findings demonstrate that the NLP-based virtual assistant can enhance efficiency and accessibility in academic services, while also reducing the workload of administrative staff. Future development will focus on expanding the knowledge base and integrating predictive analytics to support academic decision-making.
