Pendugaan Produktivitas Bagan Perahu dengan Regresi Gulud, LASSO dan Elastic-net


  • Resty Fanny Department of Statistics, IPB
  • Anik Djuraidah Department of Statistics, IPB
  • Aam Alamudi Department of Statistics, IPB



elastic-net, LASSO, multicolinearity, multiple linear regression, ordinary least square


Regression analysis is a statistical technique to examine and model the relationship between dependent variable and independent variable. Multiple linear regression includes more than one independent variable. Multicollinearity in multiple linear regression occurs when the independent variables has correlations. Multicolinearity causes the estimator by ordinary least square to be unstable and produce a large variety. Multicollinearity can be overcome by the addition of penalized regression coefficient. The purpose of this research is modeling ridge regression, LASSO, and elastic-net. Data which is data of fisherman catch at Carocok Beach of Tarusan Sumatera Barat as dependent variable and amount of labor, amount of fuel, volume of fishing/waring boat, number of catches, ship size, number of boat wattage, sea experience, education and age of fisher as independent variables. The best model provided by LASSO that has a RMSEP value of validated regression model is minimum than ridge regression and elastic-net. LASSO shrinked amount of labor, amount of fuel and number of wattage equal zero. There can be influence (productivity change) that is volume of fishing/waring boat and boat size that used by fisher.




How to Cite

Fanny, R., Djuraidah, A., & Alamudi, A. (2018). Pendugaan Produktivitas Bagan Perahu dengan Regresi Gulud, LASSO dan Elastic-net. Xplore: Journal of Statistics, 2(2), 7–14.

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