Implementation of random forest algorithm with parallel computing in R

Random forest is a method for building models by combining decision trees or decision trees generated from bootstrap samples and random features. A common problem that often occurs when implementing random forest is long processing time because it uses a lot of data and build many tree models to form random trees because it uses single processor. This research proposes random forest method with parallel computing and implemented in R programming language. Some of the cases used in this research are Iris flower dataset, wine quality and diabetes diagnosis data of Pima Indian woman. The results obtained from the entire study show that the computational time used when running random forest with parallel computing is shorter than when running a regular random forest using only a single processor.