Forecast Analysis of Gross Regional Domestic Product based on the Linear Regression Algorithm Technique - Delimah Pasaribu, Veta Lidya; Septiani, Fauziah; Rahayu, Suharni; Lismiatun, L; Arief, Muhammad; Juanda, Angga; Sunaryo, M. Yusuf; Rahim, Robbi (TEM Journal, 2021)
Statistical data are indispensable for macro-economic planning activities such as the Gross Regional Domestic Product (GRDP) where data can determine the economic development strategies and policies that have been adopted and can be continued in the future. This study draws on quantitative data sources from the Regional Statistical Agency of Jakarta for the period 2017-2019, the subject of the Gross Regional Domestic Product based on current business prices. The aim of this research is to test and predict the level of accuracy of GRDP at current prices based on business fields using the Linear Regression method supported by Rapid Miner software. The results show that the validated Linear Regression algorithm with K-Fold values from 2 to 10 with the sampling type linear sampling and shuffled sampling can be used and implemented with the smallest Root Mean Square Error value of IDR 9,977,431 at k = 10 for the sampling.