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All Journal Journal of Information Technology and Computer Science
Dini Eka Ristanti
Brawijaya University, Malang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Early Stunting Detection System for Toddlers Based on Height and Weight Using Backpropagation Neural Network Method Dini Eka Ristanti; Dahnial Syauqy; Barlian Henryranu Prasetio
Journal of Information Technology and Computer Science Vol. 7 No. 3: December 2022
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

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Abstract

Stunting is a chronic nutritional problem characterized by height and weight problems. Toddlers who have height and weight of more than minus 2 standard deviations are at risk of suffering from stunting and require monitoring for 3 to 6 consecutive months. Currently, the system still measures toddlers' height and weight, then matches it with the World Health Organization(WHO) growth data table. Therefore, we proposed to develop a system to detect height and weight as well as the risk of stunting in toddlers using an ultrasonic sensor, load cell, and backpropagation algorithm. In its implementation, the ultrasonic sensor achieves an accuracy of 99%, and the load cell reaches 93%. The system uses backpropagation neural network method, which achieved an R of 0.99845 using 3 inputs, 16 hidden layers,1 layer for re-weighting, and 1 output layer. The mean squared error reaches 0.01 with 2 prediction classes, low risk, and high riskstunting. Overall, the total system accuracy can reach 97.75%.
Co-Authors Barlian Henryranu Prasetio Dahnial Syauqy