The Detection of Stunting Anomalies in Toddler by Computer Vision
DOI:
https://doi.org/10.26699/jnk.v11i1.ART.p099-104Keywords:
stunting, z-score, mobile mediaAbstract
The golden age is the most important period that all children go through. At this time, parents need to optimize their child's growth and development. The nutritional adequacy of toddlers must be monitored to detect abnormalities such as stunting, wasting, obesity and malnutrition. Stunting is a condition in young children where height or body length as measured by Z-score does not correspond to age. In today's digital era, healthcare generates large amounts of data every day. This data is in various forms, including text, numbers, and digital images or videos. Computer vision in health care is a field of artificial intelligence that allows computers to interpret and act on visual data, including monitoring the growth and development of toddlers. Computer vision can be used to analyze data on stunting status of toddlers. The aim of this research was to develop mobile media to be able to screen and monitor stunting in toddlers using computer vision. The type of the research was research and development methods where the function of using this method was for product validation and development, with the dependent indicator being stunting toddlers. The results of this research showed an accuracy of 90.5%. These results showed that the application of computer vision and artificial neural networks to predict stunting anomalies in toddlers could be used and showed good results. It is hoped that in the future this application can be used by the government, midwives and cadres to continuously monitor toddler stunting
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