Climate Prediction Using RNN LSTM to Estimate Agricultural Products Based on Koppen Classification

Novia Andini, Wiranto Herry Utomo

Abstract


The yield of an agricultural process is very important and influential, where the harvest is used as a support for human life both as food and a source of income. Many factors can influence the success of agriculture, such as the climate that is going on around in the surrounding area. The wrong prediction in determining the future climate will cause crop failure due to incompatibility with the type of plant. In this era, many technologies have been able to predict climate, one of which is technology machine learning that has many types and techniques, which machine learning technology has been widely used in predicting many things. This study aims to predict the climate in an area which is intended to determine crop yields based on the Koppen classification, and also the prediction based on several parameters such as temperature, humidity, duration of sun exposure and rainfall. And the results of this study is have a loss of 0.006 and with the MAPE value as an indicator of the percentage error and as an indicator for determining the accuracy of the prediction results, which is 3.29%, which means that it is included in the very accurate category in predicting climate to estimate agricultural yields.


Keywords


Climate Prediction, Long Short Term Memory (LSTM), Recurrent Neural Network (RNN), Koppen Classification

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References


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DOI: https://doi.org/10.31326/jisa.v4i2.911

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