Oil Palm Yield Forecasting Based on Weather Variables Using Artificial Neural Network
Forecasting of oil palm yield has become a main factor in the management of oil palm industries for proper planning and decision making in order to avoid monthly high cost in harvesting. Predicting future value of oil palm yield with minimum error becomes an important issue recently. A lot of factor...
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| Main Authors: | , , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2016-09-01.
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| Subjects: | |
| Online Access: | Get fulltext |
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| LEADER | 02419 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs4120_885 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Kartika, Nadia Dwi |e author |
| 700 | 1 | 0 | |a Astika, I Wayan |e author |
| 700 | 1 | 0 | |a Santosa, Edi |e author |
| 245 | 0 | 0 | |a Oil Palm Yield Forecasting Based on Weather Variables Using Artificial Neural Network |
| 260 | |b Institute of Advanced Engineering and Science, |c 2016-09-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/4120 | ||
| 520 | |a Forecasting of oil palm yield has become a main factor in the management of oil palm industries for proper planning and decision making in order to avoid monthly high cost in harvesting. Predicting future value of oil palm yield with minimum error becomes an important issue recently. A lot of factors determine the productivity of oil palm and weather variables play an important role that affect plant growth and development that may reduce yield significantly. This research used secondary data of yield and weather variables available in company administration. It proposed feed forward neural network with back propagation learning algorithm to build a monthly yield forecasting model. The optimization procedure of ANN architecture obtained the best using 60 neurons in input layer, five hidden layers and one neuron in the output layer. Training data were from January 2005 to June 2008 while testing data were from July 2008 to December 2009. ANN architecture using five hidden layers gave the best accuracy with MAE 0.5346 and MSE 0.4707 while the lowest accuracy occurred by using two hidden layers with MAE 1.5843and MSE 4.087. | ||
| 540 | |a Copyright (c) 2016 Indonesian Journal of Electrical Engineering and Computer Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Technology; Computer Engineering; Machine Learning | ||
| 690 | |a Neural Network; | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 3, No 3: September 2016; 626-633 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v3.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/4120/885 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/4120/885 |z Get fulltext |