• Reka Agustia Astari IPB University, Indonesia
  • Megawati IPB University, Indonesia
  • Setyo Wahyudi IPB University, Indonesia



spatial heterogeneity global , collinearity, geographically weighted regression , geographically weighted lasso


Geographically Weighted Lasso (GWL) is a combination of two regression methods, namely Geographically Weighted Regression (GWR) and Least Absolute Shringkage Selection Operator (LASSO). Both methods have their own uses. GWR is a regression that takes into account the geographical location aspect because the spatial heterogeneity test is not met. LASSO is a regression method to overcome multicollinearity in the data. The two problems are simultaneously contained in one regression model, namely the GWL method. This study will analyze the factors that affect rice production in 34 provinces in Indonesia by applying and interpreting the results of the Geographically Weighted Lasso method. The results of the analysis show that the coefficient of determination of the GWL model is 0.9703 so it can be concluded that the explanatory variables in this study can that the global level of rice production in each province in Indonesia is 97.03%.


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How to Cite

Agustia Astari, R., Megawati, & Wahyudi, S. (2024). IMPLEMENTATION OF GEOGRAPHICALLY WEIGHTED LASSO (GWL) IN ANALYZING RICE PRODUCTION FACTORS IN INDONESIA. Journal of Statistical Methods and Data Science, 1(2).