Implementation of Random Forest Algorithm on Palm Oil Price Data

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    Arif Rahman Hakim( 1 ) Dewi Marini Umi Atmaja( 2 ) Amat Basri( 3 ) Muhamad Syafii( 4 )

    (1) Universitas Medika Suherman | Indonesia
    (2) Universitas Medika Suherman | Indonesia
    (3) Universitas Buddhi Dharma | Indonesia
    (4) Universitas Budi luhur | Indonesia


One of the potential commodities that are widely cultivated in Indonesia is palm oil, and palm oil or commonly referred to as palm oil is one of the processed products of palm oil which generates the most important foreign exchange for Indonesia. Data mining is a process that utilizes mathematical techniques, statistics, artificial intelligence, and machine learning techniques to extract and identify useful information and related knowledge from large databases [3], including palm oil price data. Random Forest is one of the methods in the decision tree. A decision tree is a flowchart shaped like a tree with a root node that is used to collect data that is used to solve problems and make decisions. In this study, a random forest algorithm was used to classify palm oil price data from 2014 to 2019. The classification method used the random forest algorithm on palm oil data using the Mtry parameter of 1 and the Ntree parameter of 500 resulting in an accuracy percentage of 100%. The most influential variable (importance variable) in the classification model using the resulting random forest algorithm is the palm oil variable.


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