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All Journal International Journal of Basic and Applied Science
Binti Mohamad, Mumtazimah
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Physics

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Classification of stunting for early childhood in indramayu using machine learning methods Krisnanik, Erly; Cholil, Widya; Adrezo, Muhammad; DP, Catur Nugrahaeni; Binti Mohamad, Mumtazimah
International Journal of Basic and Applied Science Vol. 14 No. 2 (2025): Sep (In Progress)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i2.833

Abstract

The stunting prevalence rate in 2020 of the Ministry of Health of the Republic of Indonesia was 38.9%. The stunting prevalence rate in Central Java itself is 33.9%, of which 17.0% are stunted and 16.9% are very short. The purpose of the study is to obtain valid data on the factors causing stunting and carry out the classification process quickly. The method used in this study is machine learning by comparing three algorithms, namely: SVM, KNN and Random Forrest. The results of this study are said that the average calculation of the accuracy level of early childhood stunting data using SVM and KNN is above 80% and Random Forrest is below 80%. While the calculation results of the average precision value of 84% and recall value of 80% using SVM, the average precision value of 95% and the recall value of 91% using KNN with K = 1, and the average precision value of 87% and the recall value of 52% using Random Forrest.  The conclusion of the comparison between SVM, Random Forest and KNN methods to calculate precision and recall values can be said that KNN is better with K = 1 close to 100%.
Co-Authors DP, Catur Nugrahaeni Erly Krisnanik Muhammad Adrezo Widya Cholil