A Study of Prediction Model for Capture Fisheries Production in Indonesian Sea Waters Using Machine Learning

Abstract

The potential for capture fisheries in Indonesia is a priceless wealth. This wealth has not been explored optimally. Fisheries resources are included in the category of renewable resources whose sustainability needs to be considered. This is important in maintaining food security which will increase over time, due to population growth. Capture Fisheries Production Prediction Model is needed to find out what determining variables affect capture fisheries production. There are many methods for predicting, the method that is widely used today is using machine learning since it ability to handle complex jobs with large input data. This research is a literature study, which aims to: (1) identify and analyze machine learning methods that are suitable for predicting capture fisheries production, and (2) identify variables that can affect capture fisheries production. The results of the study show that the Neural Network method is most widely used as a predictive model. In addition, the Random Forest and Linear Logistics methods provide better accuracy results. The results of the study also succeeded in finding 12 determining variables for the capture fisheries production prediction model.

Keywords

• capture fisheries production
• machine learning
• prediction models