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All Journal ROTOR: JURNAL ILMIAH TEKNIK MESIN Tech-E Jurnal Review Pendidikan dan Pengajaran (JRPP) Community Development Journal: Jurnal Pengabdian Masyarakat ETNIK : Jurnal Ekonomi dan Teknik Jurnal Ekonomi dan Bisnis
Syafii, Muhamad
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Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Other

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Journal : Tech-E

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Implementation of Random Forest Algorithm on Palm Oil Price Data Rahman Hakim, Arif; Atmaja, Dewi Marini Umi; Basri, Amat; Syafii, Muhamad
Tech-E Vol. 6 No. 2 (2023): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v6i2.1757

Abstract

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.
Co-Authors Ali, Sulaiman Ariadi, Wa Arif Rahman Hakim Arif, Zainal Atmaja, Dewi Marini Umi Bambang Nugroho Basri, Amat Dyah Nugraheny Priastuti Febrian, Wenny Desty Gandariani, Titis Hendra Sudarso Imburi, Christian Soleman Jayawarsa, A.A. Ketut Latuny, Leny Sopia Mas'ud Muhammadiah Meny Sriwati, Meny Merizawati, Henny Nainggolan, Hermin Nurul Hikmah Pamungkas, Nurman Putranto, Algooth Rahayu, Diah Afrianti Rerung, Ahadi Roza, Nelli Sani, Indra Solissa, Everhard Markiano St Rahmah Suprihartini, Yayuk Surawan, Surawan Suyana, Nana Tanudy, Ayub Taryana Taryana, Taryana TEGUH ARIFIANTO, TEGUH Toatubun, Najarudin Wajnah, Wajnah Wakla, Ure Wartono, Tono Widodo, Syamsul Bahri