Pengembangan Model IoT dan Pendekatan Machine Learning (ML) pada Budidaya Perikanan

Abstract

Traditional fish farming faces various challenges, including a high dependence on environmental factors that affect fish growth and health, where water quality and parameter monitoring are still done manually. This research aims to analyze specific issues occurring in fish farming, develop an Internet of Things (IoT) model, apply a Machine Learning (ML) approach in fish farming, and integrate the Internet of Things (IoT) model and Machine Learning (ML) approach in fish farming. The method used involves collecting data from various sensors installed in fish ponds, such as temperature, pH, and oxygen levels. This data is then analyzed using Machine Learning algorithms to predict optimal conditions for fish growth. The research results show that this model can improve farming efficiency, reduce the risk of fish mortality, and increase harvest yields. In conclusion, the application of IoT and ML technology in fish farming can provide innovative and sustainable solutions to enhance productivity and sustainability in the fisheries sector.