Klasifikasi Gizi Anak Menggunakan Metode Random Forest Classifier

Muhammad, Yusril Aldean (2022) Klasifikasi Gizi Anak Menggunakan Metode Random Forest Classifier. Project Report. Institut Telkom Telkom Purwokerto. (Unpublished)

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Abstract

Posyandu is the front line in the implementation of health, especially for children under five and toddlers. Posyandu often conducts health education for children. immunizations for children and periodic recapitulation of children's health. Several posyandu are scattered throughout Indonesia, causing some differences in the level of progress of posyandu in terms of recapitulation and calculations for data collection of children. The need for a method that can be used by all parties to facilitate the calculation of data for consideration of the progress of children's nutrition. This study aims to create a machine learning model to be able to automatically calculate the nutritional class of children. This study uses previously available child nutrition data and processes it with a predictive model using the Random Forest Classifier Algorithm which produces an accuracy of 74%

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