Algoritma Backpropagation Neural Network dalam Memprediksi Harga Komoditi Tanaman Karet

Autor:	Masna Wati, Julius Rinaldi Simanungkalit, Novianti Puspitasari, Haviluddin Haviluddin, Herman Santoso Pakpahan
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	Alternative methods Mean squared error Artificial neural network rubber prices prediction QA75.5-76.95 Backpropagation Natural rubber mse Error tolerance visual_art Electronic computers. Computer science Statistics Function learning visual_art.visual_art_medium bpnn commodity Mathematics
Zdroj:	Ilkom Jurnal Ilmiah, Vol 12, Iss 1, Pp 32-38 (2020)
ISSN:	2548-7779 2087-1716
Popis:	Rubber plantation sector is one of the leading commodities in East Kalimantan Province contributing greatly to non-oil and gas exports. Currently, the price of rubber in the world is increasingly competitive. The aim of this research is to predict the rubber prices as a reference for the government and companies in making policies and preparing work plans. Data of 60 months during the period of 2014-2018 taken from Plantation office of East Kalimantan Province has been analyzed using Backpropagation Neural Network (BPNN) algorithm in predicting rubber prices. Based on the testing results, parameters of the BPNN algorithm with ratio of 4: 1, architectural models 5-10-10-10-1, trainlm learning function, learning rate of 0.5, error tolerance of 0.01, and epoch of 1000 have gained good accuracy with a mean square error (MSE) of 0.00015464. The results showed that the BPNN algorithm can be used as an alternative method in forecasting.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e38a1f4a72a3fccdf523f7575a080cf3 http://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/521 Zobrazit plný text záznamu