Implementation of random forest algorithm with parallel computing in R

N. Azizah, L. S. Riza*, Y. Wihardi

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Article number022028
JournalJournal of Physics: Conference Series
Issue number2
Publication statusPublished - 22 Nov 2019
Externally publishedYes
Event6th International Seminar on Mathematics, Science, and Computer Science Education, MSCEIS 2018 - Bandung, Indonesia
Duration: 27 Oct 2018 → …


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