<![CDATA[Indonesia is a maritime country with most of the population living near water areas. Water products are a common commodity often consumed cheaply, and food is therefore one of the primary human needs. Fishery harvest predictions are needed to control prices, prepare seeds, and ensure stable sales and consumption. The reason for choosing GRU for this prediction is that classical methods, commonly used in econometrics or time series analysis, were previously prevalent. GRU requires fewer operations than LSTM. Instead of training with an optimization algorithm relying on backpropagation and gradients, metaheuristic optimization in the form of a GA is used. GA does not require gradient information and is expected to avoid local optima. The total average MSE obtained is 9.55%.]]>
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