Determining Toddler’s Nutritional Status with Machine Learning Classification Analysis Approach

Taufik Hidayat; Mohammad Ridwan; Muhamad Fajrul Iqbal; Sukisno Sukisno; Robby Rizky; William Eric Manongga

doi:10.30812/matrik.v24i2.4092

Taufik Hidayat Universitas Islam Syekh-Yusuf, Tangerang, Indonesia
Mohammad Ridwan Universitas Islam Syekh-Yusuf, Tangerang, Indonesia
Muhamad Fajrul Iqbal Universitas Islam Syekh-Yusuf, Tangerang, Indonesia
Sukisno Sukisno Universitas Islam Syekh-Yusuf, Tangerang, Indonesia
Robby Rizky Universitas Mathla'ul Anwar, Banten Indonesia
William Eric Manongga Chaoyang University of Technology, Taichung City, Taiwan

DOI: https://doi.org/10.30812/matrik.v24i2.4092

Keywords: Analysis model, Classification, Machine learning, Nutritional status, Toddlers

Abstract

The nutritional status of toddlers is a common issue many countries face worldwide. Various facts indicate that malnutrition is a primary focus for many researchers. Several efforts have been made to address this problem, including developing analytical models for identification, classification, and prediction. This study aims to evaluate the nutritional status of children by utilizing a classification analysis approach using Machine Learning. This research aims to improve the accuracy of the classification system and facilitate better decision-making in stunted toddlers, which is a priority, especially in the health sector. The Machine Learning classification analysis process will later utilize the performance of the Naive Bayes algorithm, the Support Vector Machine algorithm, and the Multilayer Perceptron algorithm. ML performance can be optimized using gridsearchCV to produce optimal classification analysis patterns. The data set of this study uses 6812 toddler data sourced from the Health Center at the Tangerang Regency Health Office. Based on the research presented, Machine Learning performance in analyzing nutritional status classification provides maximum results. The results are reported based on a precision level with an accuracy of 88%. The results of this analysis can also present a classification of nutritional status based on knowledge. This study can contribute to and update the analysis model in determining nutritional status. The results of this study can also provide benefits in handling nutritional status problems that occur in children.

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