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Development of Artificial Neural Network Model for Estimation of Salt Fields Productivity

Indra Cahyadi, Heri Awalul Ilhamsah, Ika Deefi Anna


Abstract


In recent years, Indonesia needs import million tons of salt to satisfy domestic industries demand. The production of salt in Indonesia is highly dependent on the weather. Therefore, this article aims to develop a prediction model by examining rainfall, humidity and wind speed data to estimate salt production. In this research, Artificial Neural Network (ANN) method is used to develop a model based on data collected from Kaliumenet Sumenep Madura.  The model analysis uses the full experimental factorial design to determine the effect of the ANN parameter differences. Then, the selected model performance compared with the estimate predictor of Holt-Winters. The results present that ANN-based models are more accurate and efficient for predicting salt field productivity.

Keywords


Jaringan Syaraf Tiruan; Peramalan; Model Prediksi; Ladang Garam; Manajemen Rantai Pasok

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DOI: https://doi.org/10.22219/JTIUMM.Vol20.No2.152-160 | Abstract views : 189 | PDF views : 189 |

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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


 
Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.