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All Journal Brilliance: Research of Artificial Intelligence
Suryana, Syahro
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Decision Sciences, Operations Research & Management Mathematics Other

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Linear Regression Algorithm Analysis for Predicting Electrical Panel Painting Quality Susilo, Arif; Widodo , Edy; Rilvani, Elkin; Suryana, Syahro
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4096

Abstract

Industry is increasingly developing rapidly and has an impact on the emergence of competition between companies, both private and state, both companies engaged in manufacturing and service companies. Linear Regression is used to find out how the dependent/criterion variable can be predicted through independent variables or predictor variables, individually. Based on the results of the tests that have been carried out, the variables or attributes used in this research (minute and thinkness results) have a significant effect on this research. It is proven that using the linear regression algorithm is able to provide good results with a Root Mean Squared Error value of 0.273 +/- 0.000. This is because there is a correlation or functional relationship (cause - effect) between one variable (dependent or criterion) and another variable (independent or predictor). This testing process is carried out to identify stock needs using a linear regression algorithm
Linear Regression Algorithm Analysis for Predicting Electrical Panel Painting Quality Susilo, Arif; Widodo , Edy; Rilvani, Elkin; Suryana, Syahro
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4096

Abstract

Industry is increasingly developing rapidly and has an impact on the emergence of competition between companies, both private and state, both companies engaged in manufacturing and service companies. Linear Regression is used to find out how the dependent/criterion variable can be predicted through independent variables or predictor variables, individually. Based on the results of the tests that have been carried out, the variables or attributes used in this research (minute and thinkness results) have a significant effect on this research. It is proven that using the linear regression algorithm is able to provide good results with a Root Mean Squared Error value of 0.273 +/- 0.000. This is because there is a correlation or functional relationship (cause - effect) between one variable (dependent or criterion) and another variable (independent or predictor). This testing process is carried out to identify stock needs using a linear regression algorithm
Co-Authors ARIF SUSILO Elkin Rilvani Widodo , Edy