Predict Farmer Exchange Rate in the Food Crop Sector Using Principal Component Regression

Farmer Exchange Rate (FER) in Indonesia is very concerning. According to BPS data, there are various regions that experience increases and decreases every year. The goal of this paper predict the food crop sector using Principal Component Regression (PCR) since multicollinearity data. Therefore, with PCR method data based on 33 different provinces can determine supporting factors. model used help farmers be able improve welfare economic growth as it depends farmers. Further analysis found Harvest Area, production, Human Development Index had an effect farmer exchange rate. By model, expected have increasing level solve problem.