Spatial Analysis and Machine Learning Integration for Nutritional Status Mapping Using ANN and Random Forest Models

Authors

Desi Anis Anggraini, Department of Informatics Engineering, Universitas Islam Negeri Maulana Malik Ibrahim, Malang, IndonesiaFollow
Fachrul Kurniawan, Department of Informatics Engineering, Universitas Islam Negeri Maulana Malik Ibrahim, Malang, IndonesiaFollow
Fresy Nugroho, Department of Informatics Engineering, Universitas Islam Negeri Maulana Malik Ibrahim, Malang, IndonesiaFollow
Meidya Koeshardianto, Department of Informatics, University of Trunojoyo Madura, Bangkalan, 69162, IndonesiaFollow
Mohammad Iqbal Bachtiar, University of KH. Bahaudin Mudhary Madura, SumenepFollow

Abstract

Nutritional problems among children under five remain a major public health challenge. This research seeks to create a spatially oriented system for evaluating and mapping nutritional status utilizing Artificial Neural Network (ANN) and Random Forest (RF) algorithms. Data obtained from the Sumenep District Health Office included age, weight, height, and gender variables. Both models were trained using a 70:30 data ratio and evaluated with accuracy, precision, recall, and F1-score metrics. The ANN model achieved an accuracy of 95.8%, while the RF model reached 97.7%. Classification results were visualized through a Geographic Information System (GIS) to illustrate spatial distribution and identify high-risk zones. The integration of machine learning and spatial analysis proved effective in enhancing classification accuracy, improving data interpretation, and supporting data-driven nutritional policy and regional health decision-making.

DOI

https://doi.org/10.17977/um018v8i12025p1-15

First Page

1

Last Page

15

Recommended Citation

Anggraini, Desi Anis; Kurniawan, Fachrul; Nugroho, Fresy; Koeshardianto, Meidya; and Iqbal Bachtiar, Mohammad (2025) "Spatial Analysis and Machine Learning Integration for Nutritional Status Mapping Using ANN and Random Forest Models," Knowledge Engineering and Data Science: Vol. 8: No. 1, Article 1.
DOI: https://doi.org/10.17977/um018v8i12025p1-15
Available at: https://citeus.um.ac.id/keds/vol8/iss1/1