Optimization of Machine Learning Classification Analysis of Malnutrition Cases in Children

Optimization of Machine Learning Classification Analysis of Malnutrition Cases in Children

analysis of classification; malnutrition, artificial neural network (ANN); multilayer perceptron (MLP); west
sumatra province

Malnutrition is one of the problems that occurs in children caused by a lack of nutritional intake. Indonesia contributed 36%,
making it the fifth country with the largest cases of malnutrition in the world. Based on this, a solution is needed to reduce the
growth rate of malnutrition cases. This research aims to carry out classification analysis to determine nutritional status by
optimizing Machine Learning (ML) performance. The ML classification analysis process will later utilize the performance of
the Artificial Neural Network (ANN) method with the Multilayer Perceptron (MLP) algorithm. ML performance can be
optimized using the Pearson Correlation (PC) method to produce optimal classification analysis patterns. This research
dataset uses child nutrition case data of 576 patients sourced from the M. Djamil Padang Province Regional General Hospital
(RSUP). The dataset is divided into 417 training data and 159 test data. Based on the tests that have been carried out, the
performance of the PC method can provide precise and accurate analysis patterns. This analysis pattern has also been able to
provide a fairly good level of accuracy, namely 95%. Not only that, this research is also able to present analysis patterns with
the best ANN architectural model in classifying nutritional status. Based on the overall results, this research can be used as
an alternative solution to handling nutritional problems in children.

Musli Yanto, Febri Hadi, Syafri Arlis

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

December 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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