Implementation of the Long Short-Term Memory Algorithm in a Chatbot for Dengue Fever Information and Education Services
Year: 2025
Type: article
Abstract: Dengue fever is one of the most common infectious diseases in Indonesia. However, information regarding its prevention and treatment remains fragmented, with limited accessibility and availability. This study developed a chatbot integrating the Long Short-Term Memory (LSTM) algorithm to answer various questions about dengue fever, including its symptoms, prevention, and treatment. The dataset used consists of questions and answers related to dengue fever, sourced from both primary and secondary data. The data undergoes a series of preprocessing steps before being used for model development, training, and evaluation. The test results indicate that the developed model achieved an accuracy of 100% during validation with a loss function value of 0.0221. These findings demonstrate that the LSTM-based chatbot can provide accurate and relevant responses, making it an effective tool for educating the public in an interactive and efficient manner. This implementation is expected to offer an innovative solution for increasing public awareness of dengue fever prevention and management.
Source: SISTEMASI
Authors: Heru Budianto, Fahmi Yusuf, Dede Irawan, Muhamad Akhirul Sidik, Aiena Nurhasanah, Sabrina Mauldya, Silmi Nur Afifah
Institution: Universitas Kuningan
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408317987