Random Forest Algorithm for Toddler Nutritional Status Classification Website - Fatmawati, Maylia; Herlambang, Bambang Agus; Nada, Noora Qotrun (Politeknik Negeri Batam, 2024)
Accurate data processing is essential for classifying toddler nutritional status on a website platform. The Random Forest algorithm is particularly effective in this context due to its ability to manage large datasets and mitigate overfitting. This study leverages Flask as the web framework to ensure responsiveness and adaptability, optimizing the data processing experience for users. Using secondary data comprising 120,999 records, the research aims to answer: