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All Journal Bulletin of Social Informatics Theory and Application
Herman Yuliansyah
Faculty of Industrial Technology, Universitas Ahmad Dahlan, Indonesia
Computer Science & IT Social Sciences

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Artificial intelligence in malnutrition research: a bibliometric analysis Herman Yuliansyah; Sulistyawati; Tri Wahyuni Sukesi; Surahma Asti Mulasari; Wan Nur Syamilah Wan Ali
Bulletin of Social Informatics Theory and Application Vol. 7 No. 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v7i1.605

Abstract

Malnutrition is a nutritional imbalance in a child’s body. Currently, there have been many reviews done on malnutrition in children. However, reviews on artificial intelligence linked with malnutrition are yet to be done. Thus, this study aims to identify the implementation of artificial intelligence in predicting malnutrition using bibliometric analysis. The bibliometric analysis consists of four stages: determining the purpose and scope, selecting the analytical technique, collecting data, and presenting the findings. Data used for this analysis is sourced from the Scopus database. The investigation was conducted using VOSviewer and “Publish or Perish” software. Based on five searched words: malnutrition, artificial intelligence, machine learning, neural networks, and deep learning, it was found that machine learning is the most widely used artificial intelligence approach for malnutrition research. Deep learning techniques are reported to grow as it is introduced as a new method in artificial intelligence. Malnutrition prediction tasks are the most studied problem. The use of deep learning, reinforcement learning, and transfer learning methods are used tremendously in malnutrition prediction research. This analysis’s results help improve the quality of the review by showing the mapping areas for malnutrition research.
Co-Authors Sulistyawati Sulistyawati Surahma Asti Mulasari Tri Wahyuni Sukesi Wan Nur Syamilah Wan Ali