Prediction of fishers’ income using a flexible model in Karanggongso fishers community, Trenggalek regency, Indonesia

The contribution of fisheries to the national GDP had increased from 2.32% in 2014 to 2.60% in 2018. However, in 2020, the threat of the Covid-19 pandemic emerged, which hit all sectors of the economy, including the fisheries sector. Many communities, especially coastal fishing communities, are complaining about economic hardship. Income has fallen dramatically because people’s purchasing power has fallen significantly. Based on these problems, this research was conducted to build a fishers’ income prediction model. This research took a case study on fishers in Karanggongso District Trenggalek by surveying 50 fishing households. There were 12 predictors variables, namely Boat Type (X1), Price Boat (X2), Age of the Boat (X3), Boat Power (X4), Machine Price (X5), Engine Life (X6), Fishing Equipment Price (X7), Fishing Gear Life (X8), Cool Box (X9), Trip/week (X10), Average Hours/trip (X11), and Total Expenditures/week (X12). The response variable is Income Per Week (Y). Data analysis was done by using multiple linear regression analysis and flexible modelling with a machine learning approach. Based on the results of the analysis, a multiple linear regression model had an accuracy level of R 2 = 70.5% and MSE = 1.086 × 10 18 with the boat price was the most dominant influence on fishers’ income. While flexible modelling has an accuracy level of R 2 = 85.2% and MSE = 3.308 × 10 14 . From this research, it was proven that the flexible model had a higher level of accuracy than the linear regression model. Also, the flexible model obtained the nonlinear effect on the number of cool boxes and the fishing gear life.