Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022)

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Prediction of Oil Palm Production Using Recurrent Neural Network Long Short-Term Memory (RNN-LSTM)

Authors
Muhdan Syarovy1, 2, Andri Prima Nugroho1, *, Lilik Sutiarso1, Suwardi1, 3, Mukhes Sri Muna1, Ardan Wiratmoko1, Sukarman3, Septa Primananda3
1Smart Agriculture Research, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jln. Flora No.1 Bulaksumur, Yogyakarta, 55281, Indonesia
2Indonesia Oil Palm Research Institute, Jln. Brigjen Katamso, No. 51, Medan, 20158, Indonesia
3Wilmar International Plantation, Region Kalimantan Tengah, Indonesia
*Corresponding author. Email: andrew@ugm.ac.id
Corresponding Author
Andri Prima Nugroho
Available Online 22 May 2023.
DOI
10.2991/978-94-6463-122-7_6How to use a DOI?
Keywords
Artificial Intelligence; RNN; LSTM; Oil Palm; Prediction
Abstract

Prediction of oil palm production is essential so all activities can be planned effectively and efficiently, especially in financing. One can do many ways, one of which is utilizing production history data using the Recurrent Neural Network – Long Short-Term Memory (RNN-LSTM) model. RNN-LSTM is a Deep Learning model that can be used to predict based on sequential data. This study aims to see the performance of the RNN-LSTM model in predicting oil palm production. The annual production history data for 11 years from the division and estate levels were used. There were four inputs tested from the data used, namely 3, 5, 7, and 9 inputs. The results showed that nine inputs could predict well with MSE, MAE, and MAPE, respectively 1.186, 0.732, and 0.030 at the time of model validation and 39.711, 4.210, and 0.154 at the time of model evaluation.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022)
Series
Advances in Biological Sciences Research
Publication Date
22 May 2023
ISBN
10.2991/978-94-6463-122-7_6
ISSN
2468-5747
DOI
10.2991/978-94-6463-122-7_6How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Muhdan Syarovy
AU  - Andri Prima Nugroho
AU  - Lilik Sutiarso
AU  - Suwardi
AU  - Mukhes Sri Muna
AU  - Ardan Wiratmoko
AU  - Sukarman
AU  - Septa Primananda
PY  - 2023
DA  - 2023/05/22
TI  - Prediction of Oil Palm Production Using Recurrent Neural Network Long Short-Term Memory (RNN-LSTM)
BT  - Proceedings of the 3rd International Conference on Smart and Innovative Agriculture (ICoSIA 2022)
PB  - Atlantis Press
SP  - 55
EP  - 66
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-122-7_6
DO  - 10.2991/978-94-6463-122-7_6
ID  - Syarovy2023
ER  -