Open AccessFarmer exchange rate category: A Prediction analysis using ANN back propagation
Author(s) -
Syaharuddin Syaharuddin,
Z Azis,
Suvriadi Panggabean,
S W Dachi,
- Nurhayati,
Suwati,
Mulono Apriyanto,
Ria Resti Bekti Utami
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/926/1/012002
Subject(s) - exchange rate , welfare , government (linguistics) , economics , artificial neural network , agricultural economics , agricultural science , econometrics , statistics , business , mathematics , computer science , environmental science , finance , artificial intelligence , linguistics , philosophy , market economy
The Farmer Exchange Rate (FER) is one of the indicators in determining the welfare level of farmers. Every month this large FER has a change especially at the end of each season during harvest, the price game especially from producers often occurs so that not a few farmers complain and suffer losses. Therefore, there need to be efforts from the government is looking at past data trends to find the amount of FER in the future so that policies can side with farmers. Artificial Intelligent with a multilayer Back Propagation method is very good for modeling and forecasting of data series in the past with data input in the form of a matrix mxn. The simulation results show that in 2020 in NTB there is an average increase in the exchange rate of farmers of 118.76%, this means that farmers are experiencing a surplus or farmers’ incomes are rising more than their expenditures. However, it appears that from June-August and October-November there was an average decrease of 2%, although overall there was increase of 2.076%. This result was obtained by the construction of a network architecture of two hidden layers where theMSE of 0.12, and MAPE of 0.23.