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All Journal Jurnal Tekinkom (Teknik Informasi dan Komputer)
Dinda Betaria Hutagaol
Universitas Prima Indonesia
Computer Science & IT

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KLASIFIKASI PENYAKIT STUNTING DENGAN MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN RANDOM FOREST Mahmin Banurea; Dinda Betaria Hutagaol; Oloan Sihombing
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.927

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.
Co-Authors Mahmin Banurea Oloan Sihombing