Forecast Analysis of Gross Regional Domestic Product based on the Linear Regression Algorithm Technique

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 sour...

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Published in:TEM Journal Vol. 10; no. 2; pp. 620 - 626
Main Authors: Delimah Pasaribu, Veta Lidya, Septiani, Fauziah, Rahayu, Suharni, Lismiatun, L, Arief, Muhammad, Juanda, Angga, Sunaryo, M. Yusuf, Rahim, Robbi
Format: Journal Article
Language:English
Published: Novi Pazar UIKTEN - Association for Information Communication Technology Education and Science 01.05.2021
Subjects:
Algorithms
Data mining
Datasets
Experiments
Fisheries
GDP
Gross Domestic Product
ICT Information and Communications Technologies
Mean square errors
Prices
Regression analysis
Retailing industry
Software
Water supply
Data Mining, Linear Regression
Root Mean Square Error
Gross Regional Domestic Product
ISSN:2217-8309, 2217-8333
Online Access:Get full text
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Abstract 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.
AbstractList 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.
Author Rahim, Robbi
Lismiatun, L
Juanda, Angga
Delimah Pasaribu, Veta Lidya
Sunaryo, M. Yusuf
Septiani, Fauziah
Rahayu, Suharni
Arief, Muhammad
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Gross Regional Domestic Product
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SubjectTerms Algorithms
Data mining
Datasets
Experiments
Fisheries
GDP
Gross Domestic Product
ICT Information and Communications Technologies
Mean square errors
Prices
Regression analysis
Retailing industry
Software
Water supply
Title Forecast Analysis of Gross Regional Domestic Product based on the Linear Regression Algorithm Technique
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