Smart Aquaculture Design for Vannamei Shrimp Farming Based on Quality Function Development

Authors

  • Budi Setiawan Bina Nusantara University
  • Nico Surantha Bina Nusantara University

DOI:

https://doi.org/10.21512/commit.v18i2.9466

Keywords:

Smart Aquaculture Design, Vannamei Shrimp, Quality Function Development (QFD)

Abstract

In the fishery industry, Indonesia’s large water area has the potential for developing and cultivating fisheries such as vannamei shrimp. For this reason, aquaculture, particularly vannamei shrimp farming, can play a crucial role in Indonesia’s economy and food supply. However, challenges such as fluctuating water quality, disease outbreaks, turbidity levels, and irregular shrimp feeding schedules in ponds can affect the productivity and sustainability of shrimp farming. The smart aquaculture system integrates technologies, such as IoT-based sensors, automated feeding mechanisms, and real-time water quality monitoring to optimize the farming process. The research proposes a smart aquaculture design for vannamei shrimp farming based on the Quality Function Development (QFD) method. It starts by creating questionnaires to identify stakeholders’ level of interest. The questionnaire results are used as a reference for system redesign using the QFD method to improve the quality and quantity of shrimp harvest, cultivating effectively and efficiently and helping and facilitating the supervision of pond managers on pond water quality, feeding, and feed availability. The result highlights the application of QFD in creating a tailored, technology-driven solution that supports better decision-making, resource optimization, and improved shrimp health. The system reduces human error, enhances farm management, and promotes higher yields by providing real-time data and automation. The evaluation results show that the proposed design can achieve high stakeholder satisfaction. It also achieves better scores compared to the other two competitor’s designs.

Dimensions

Plum Analytics

Author Biographies

Budi Setiawan, Bina Nusantara University

Industrial Engineering Department, BINUS Graduate Program – Master of Industrial Engineering

Nico Surantha, Bina Nusantara University

Computer Science Department, BINUS Graduate Program – Master of Computer Science

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Published

2024-09-30
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