Dokumen BKD TA 2024/2025 Bidang B: Jurnal Penelitian - From Traditional to Innovation: Large Language Models in Fisheries Data Extraction

Dokumen BKD TA 2024/2025 Bidang B: Jurnal Penelitian - From Traditional to Innovation: Large Language Models in Fisheries Data Extraction

• By Faisal , Muhammad;
• Jurnal
• 3

The fisheries industry is crucial for the global
economy, providing essential food resources and livelihoods.
However, accurate data collection from fishermen's narrative
reports is challenging due to their unstructured nature and
low technological literacy among fishermen. This study
explores the application of Large Language Models (LLMs),
specifically the Merak-7B model, to automate data extraction
from these reports. Due to resource limitations, Claude AI
was used to generate narrative examples based on predefined
prompts, reflecting potential reports by fishermen.
Additionally, the study conducts a comparative analysis
between Merak-7B and Claude AI to assess accuracy and
reliability in data extraction. The Merak-7B model
demonstrates high accuracy in converting unstructured text
into structured data, thereby improving data collection
efficiency and reliability. The results indicate that Merak-7B
achieved an accuracy rate of 91% in extracting key data
points from fishermen's reports using 10 narrative examples
as samples. This approach addresses the technological
challenges faced by fishermen and supports better decisionmaking in fisheries management through enhanced data
quality. The findings suggest that LLMs can significantly
streamline data collection processes, aiding in sustainable
fisheries management.

• Katakunci
• URL http://
• Penerbit IEEE (Institute of Electrical and Electronics Engineers),

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