The aquaculture business is currently expanding in Malaysia due to domestic demand, but farm operators face issues from cost to environmental. This research aims to build a model, using factors of importance that can be used by policymakers for targeted assistance for the healthy growth of this sector. Two hundred and sixty-eight aquaculture operators were identified and a survey instrument was used to collect the necessary data. The data were used as input for the Random Forest model using machine learning techniques for the analysis. The AUC for the model is 0.87, highlighting the importance of this model as a good predictor of performance. Based on this model’s findings, the factor Farming Practices have the highest importance. This is followed by Environmental, Economic, and Institutional Influences, Cost of Feed, and Learning and Development. Factors Provision of Extension Services, Climate Change, Innovative Technologies, Supply Chain Risk Management Culture, and Societal have the least influence on performance.
The results from this study indicate that the Random Forest model can be a viable model to investigate future outcomes based on the major factors identified as input. The factors identified can serve as a useful guide for any targeted assistance and support besides serving as a useful input to any future policy directions. This research is limited in the sense it involves aquaculture operators only and a single model Random Forest, however, this limitation can be expanded in any future studies to include other stakeholders and different analytical approaches.
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