Implementation of Machine Learning Virtual Medical Assistant Using NLP for Stunting and Healthcare Efficiency in Simalungun

Abdi Rahim Damanik; P.P.P.A.N.W.Fikrul Ilmi R.H.Zer; Rahmat Zulpani; Egi Batubara

doi:10.47709/brilliance.v5i2.6475

Authors

Abdi Rahim Damanik STIKOM Tunas Bangsa, Indonesia
P.P.P.A.N.W.Fikrul Ilmi R.H.Zer STIKOM Tunas Bangsa, Indonesia
Rahmat Zulpani STIKOM Tunas Bangsa, Indonesia
Egi Batubara STIKOM Tunas Bangsa, Indonesia

DOI:

https://doi.org/10.47709/brilliance.v5i2.6475

Keywords:

Machine Learning, Stunting, Virtual Medical Stunting, NLP

Abstract

Stunting is a serious public health issue that has long-term impacts on children's physical growth and cognitive development. In the village areas of Simalungun Regency, North Sumatra Province, there are still significant limitations in access to effective health information and services. Low public awareness and a shortage of medical personnel are the main factors contributing to the suboptimal handling of stunting cases.This study aims to develop and implement a Machine Learning model based on Natural Language Processing (NLP) as a Virtual Medical Assistant to support the processes of education, early diagnosis, and health consultation related to stunting. The model is designed to understand user complaints, provide automated responses, and deliver appropriate nutritional recommendations and preventive actions.Training data were collected through interviews with the Simalungun Health Department and consultations with pediatricians, which were then used to build an NLP model focused on classifying stunting risk. Testing results for risk classification using the Random Forest algorithm with the Persen_Sangat_Pendek feature yielded an accuracy of 99%, precision of 99%, recall of 99%, and F1-score of 99%, indicating that the model is highly effective in distinguishing stunting categories. The developed Virtual Medical Assistant application also successfully responded to common public inquiries using NLP-based approaches. This research is expected to make a meaningful contribution to technology-based health services, particularly in rural areas, and serve as a model for developing similar systems in other regions facing comparable conditions.

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