Penerapan Bayesian Network dalam Memodelkan Kondisi Ekonomi Hijau Indonesia di Era Pandemi Berdasarkan Big Data - Salwa Rizqina Putri; Thosan Girisona Suganda; Setia Pramana (Seminar Nasional Official Statistics, 2021)
To support Indonesia's green economic growth, further analysis is needed regarding economic activity during the pandemic and its relationship to environmental conditions. This study aims to apply the Bayesian Network approach in modeling Indonesia's green economy conditions during the pandemic based on variables that are allegedly influential, such as economic activity, air quality, population mobility levels, and positive cases of COVID-19 obtained through big data. The Bayesian Network model that was constructed manually with the Maximum Spanning Tree algorithm was chosen as the best model with an average 5-cross validation accuracy in predicting four classes of GRDP is 0.83. The best model chosen shows that Indonesia's economic conditions in the pandemic era are directly influenced by the intensity of night light (NTL) which shows economic activity, air quality (AQI), and positive cases of COVID-19. Analysis of parameter learning shows that the economic growth of the Indonesian provinces still tends not to be in line with the maintenance of air quality so that efforts to achieve a green economy condition still have to be improved.