Machine Learning Klasifikasi Status Gizi Balita Menggunakan Algoritma Random Forest - Handayani, Putri; Abd. Charis Fauzan; Harliana, Harliana (KLIK: Kajian Ilmiah Informatika dan Komputer, 2024)
The future growth and development of toddlers is greatly influenced by nutritional problems at the age of 0-59 months. To achieve optimal health, high nutritional status is required. Improper development, insufficient energy for exercise, decreased immunity, and long-term impaired brain function can all be caused by malnutrition. In this case, the Integrated Service Center Post(Posyandu) is tasked with monitoring children's nutritional health. Anthropometric data, or human body measurements, such as height and weight, are part of this monitoring procedure. Other variables include position measurements and complaints that havebeen submitted. The aim of this research is to use the Random Forest algorithm to classify the nutritional status of children in Nglegok District. This study uses a confusion matrix to evaluate random forest yields. Four scenarios, each with training and test data, are created from the data to perform testing. The test results show that dividing 90% training data and 10% testing is the optimal scenario, with accuracy of 88.6%, precision of 88.1%, recall of 88.6%, and F1-Score of 88.2%.