Visualisasi Prediksi Prevalensi Balita Menggunakan Algoritma Random Forest Pada Lembaga Bidang Pangan
Year: 2024
Type: article
Abstract: This study aims to apply the Random Forest algorithm to predict the prevalence of malnutrition in toddlers in DKI Jakarta. This algorithm was chosen because it has been proven to provide high prediction accuracy, with an average above 80% in previous research. The research method includes stages of problem formulation, research objectives, research design, sample selection, data collection, data processing, interpretation of results, report preparation, and result presentation. The data used is sourced from the National Food Agency (BPN) totaling 3000 datasets, which are then processed through stages of selection, preprocessing, transformation, data mining using the Random Forest algorithm, and evaluation using RMSE and MAE formulas. It is expected that the results of this study will provide valuable insights into efforts to address malnutrition issues in toddlers in the DKI Jakarta area. Keywords: Prediction ,Random Forest, Prevalence of Toddlers,Nutrition
Source: Journal CERITA
Authors: Roy Mubarak, Siti Maesaroh, Mohamad Yusuf, Kelik Budiana, Muhammad Rizky Fahreza
Institution: Mercu Buana University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4407884700