TY - JOUR
T1 - Forecasting the Export and Import Volume of Crude Oil, Oil Products and Gas Using ANN
AU - Wanto, Anjar
AU - Herawan Hayadi, B.
AU - Subekti, Purwo
AU - Sudrajat, Dadang
AU - Wikansari, Rinandita
AU - Bhawika, Gita Widi
AU - Sumartono, Eko
AU - Surya, Sara
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2019/9/6
Y1 - 2019/9/6
N2 - The purpose of this study is to see the development of the volume (value) of exports and Imports of oil and gas in Indonesia in the form of estimated results for the coming years. Research data was taken from the Central Statistics Agency and the Indonesian Customs Service. Data is divided into 7 variables, namely; In the year, crude oil exports, crude oil Imports, oil exports, oil Imports, gas exports and gas Imports. The application of the method for estimating the volume of Crude Oil, Oil Products and Gas export Imports is the ANN backpropagation algorithm with 4 network architectural models namely; 12-5-1, 12-8-1, 12-10-1 and 12-14-1. The best network architectural model is 12-5-1 with an accuracy of 83% and MSE 0.0281641257. The minimum error used is 0.001-0.05 with a learning rate of 0.01. While the activation function used is bipolar and linear sigmoid with gradient descent training function.
AB - The purpose of this study is to see the development of the volume (value) of exports and Imports of oil and gas in Indonesia in the form of estimated results for the coming years. Research data was taken from the Central Statistics Agency and the Indonesian Customs Service. Data is divided into 7 variables, namely; In the year, crude oil exports, crude oil Imports, oil exports, oil Imports, gas exports and gas Imports. The application of the method for estimating the volume of Crude Oil, Oil Products and Gas export Imports is the ANN backpropagation algorithm with 4 network architectural models namely; 12-5-1, 12-8-1, 12-10-1 and 12-14-1. The best network architectural model is 12-5-1 with an accuracy of 83% and MSE 0.0281641257. The minimum error used is 0.001-0.05 with a learning rate of 0.01. While the activation function used is bipolar and linear sigmoid with gradient descent training function.
UR - http://www.scopus.com/inward/record.url?scp=85073260319&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1255/1/012016
DO - 10.1088/1742-6596/1255/1/012016
M3 - Conference article
AN - SCOPUS:85073260319
SN - 1742-6588
VL - 1255
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012016
T2 - 1st International Conference on Computer Science and Applied Mathematic, ICCSAM 2018
Y2 - 10 October 2018 through 12 October 2018
ER -