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Sinaga, Marlina Setia
Universitas Negeri Medan
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : Journal of Soft Computing Exploration

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Comparison of supervised machine learning methods in predicting the prevalence of stunting in north sumatra province Saragih, Vinny Ramayani; Arnita, Arnita; Indra, Zulfahmi; Taufik, Insan; Sinaga, Marlina Setia
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.498

Abstract

Stunting is a growth and development disorder in children caused by chronic malnutrition and repeated infections. Stunting has significant short- and long-term impacts and is one of the major health issues currently faced by Indonesia. The prevalence of stunting in North Sumatra Province is 18.9%, and the provincial government aims to reduce this prevalence to 14% by 2024. This study aims to compare the performance of several supervised machine learning methods in predicting stunting prevalence in North Sumatra Province. The data used is secondary data from 2021 to 2023, covering 33 districts/cities in the province. This study evaluates three machine learning models: Support Vector Regression (SVR), Decision Tree, and Random Forest, using evaluation metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The analysis results show that Random Forest provides the most accurate and consistent predictions, with lower MSE, MAE, RMSE, and MAPE values compared to the other models in most areas. Decision Tree yields good results in some regions but tends to produce higher errors in certain cases. SVR exhibits a more varied performance, with some regions showing higher prediction errors. Overall, Random Forest is the superior model for predicting district/city-level data, although model selection should be tailored to the data characteristics and application needs
Co-Authors Anitaria . Simanullang Arnita Arnita Arnita Arnita Br Barus, Jesika Casadae Wanda DIdi Febrian Elisabet Desy Yulianawati Hia Eri Widyastuti Erlinawaty Simanjuntak Grace Meisel Hamidah Nasution Hutapea, Brian Insan Taufik July Hartiny . Kairani, Nerli Lasker Pangarapan Sinaga Manullang, Sudianto Maristella Marpaung, Faridawaty Marpaung, Faridawaty Nadya, Fauza Oktariani Purba Restina Silalahi Ria Rahmadita Surbakti Saragih, Vinny Ramayani Silaban, Dewi Fortuna Silalahi, Restina Sri Dewi Tamara, Angga Yulita Molliq Rangkuti Zulfahmi Indra, Zulfahmi