KLASIFIKASI PENYAKIT STUNTING DENGAN MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN RANDOM FOREST

  • Mahmin Banurea Universitas Prima Indonesia
  • Dinda Betaria Hutagaol Universitas Prima Indonesia
  • Oloan Sihombing

Abstract

This research aims to utilize machine learning technology, especially the Support Vector Machine (SVM) and Random Forest algorithms to classify stunting in children. Stunting is a condition where a toddler's growth and development is hampered due to malnutrition in the first 1,000 days of life. This research uses a dataset of 6,500 data on stunting sufferers with 8 attribute columns such as age, baby's weight, baby's body length, weight, height, etc. The results of this research show that the SVM algorithm provides an accuracy of 65.6% for testing data and 62.7% for training data, while the Random Forest algorithm provides higher accuracy, namely 88.2% for testing data and 98.8% for training data. The hypertuning process of the SVM algorithm succeeded in increasing accuracy up to 81%. This research contributes to efforts to deal with stunting in children through the application of machine learning technology. The results of this research can be used as a reference in developing more precise stunting prediction and prevention models.

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Published
2023-12-27
How to Cite
BANUREA, Mahmin; HUTAGAOL, Dinda Betaria; SIHOMBING, Oloan. KLASIFIKASI PENYAKIT STUNTING DENGAN MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN RANDOM FOREST. Jurnal Tekinkom (Teknik Informasi dan Komputer), [S.l.], v. 6, n. 2, p. 540-549, dec. 2023. ISSN 2621-3079. Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/927>. Date accessed: 05 mar. 2024. doi: https://doi.org/10.37600/tekinkom.v6i2.927.
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Articles