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<p><i>Katsuwonus pelamis</i> is one of the high-value fishery commodities that plays a crucial role in food security and fisher’s income. However, unstable price fluctuations present a significant challenge, driven by various factors such as production volume and market demand. This study aims to develop a fish price prediction model using deep learning algorithms, specifically Long Short-Term Memory (LSTM), to forecast the price of <i>Katsuwonus pelamis</i> landed at Nizam Zachman Fishing Port. Previous studies have shown that deep learning techniques, especially LSTM, are highly effective in forecasting price fluctuations in fisheries, agricultural goods, and energy commodities. The results of the prediction model are expected to provide a more accurate basis for fish pricing, thereby supporting sustainable fisheries resource management. By adopting this approach, the study aims to contribute to the establishment of reference pricing mechanisms based on quantifiable catch systems and assist in the implementation of price stabilization policies within the fisheries sector.</p>

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Development of a Deep Learning Based Price Prediction Model for Katsuwonus pelamis at Nizam Zachman Fishing Port Jakarta Indonesia

  • 期刊论文

  • 发表日期：

    2025年3月28日

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  • Wirata Wirata,
  • Sugeng Hari Wisudo,
  • Muhammad Imron,
  • Yopi Novita,
  • Yaser Krisnafi,
  • Danu Sudrajat,
  • Suseno Suseno

摘要

Katsuwonus pelamis is one of the high-value fishery commodities that plays a crucial role in food security and fisher’s income. However, unstable price fluctuations present a significant challenge, driven by various factors such as production volume and market demand. This study aims to develop a fish price prediction model using deep learning algorithms, specifically Long Short-Term Memory (LSTM), to forecast the price of Katsuwonus pelamis landed at Nizam Zachman Fishing Port. Previous studies have shown that deep learning techniques, especially LSTM, are highly effective in forecasting price fluctuations in fisheries, agricultural goods, and energy commodities. The results of the prediction model are expected to provide a more accurate basis for fish pricing, thereby supporting sustainable fisheries resource management. By adopting this approach, the study aims to contribute to the establishment of reference pricing mechanisms based on quantifiable catch systems and assist in the implementation of price stabilization policies within the fisheries sector.