Penerapan Seleksi Fitur pada Deteksi Coronavirus Disease 19 (COVID-19) berbasis Random Forest - Saifudin, Aries; Nirmala, Endar; Kusyadi, Irpan (Program Studi Teknik Informatika Universitas Pamulang, 2024)
Data mining using machine learning algorithms can be used to help analyze historical data to predict COVID-19. The dataset used for predicting COVID-19 has many features, but those features have the possibility of redundancy or irrelevance that can cause a decrease in classifier performance. This research proposes a model that implements feature selection to select relevant features and can provide improved performance predictions for diagnose COVID-19. Some proposed feature selection techniques are Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Forward Floating Selection (SFFS), Sequential Forward Floating Selection ( SBFS), Sequential Backward Floating Selection (SBFS), and selectKBest. The classification algorithm used to classify is Random Forest. The model that gives the best performance value is the model that applies the SFS dan SFFS as feature selection.