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Linear Regression Algorithm Analysis for Predicting Electrical Panel Painting Quality

Authors

  • Arif Susilo Universitas Pelita Bangsa, Indonesia
  • Edy Widodo Universitas Pelita Bangsa, Indonesia
  • Elkin Rilvani Universitas Pelita Bangsa, Indonesia
  • Syahro Suryana Universitas Pelita Bangsa, Indonesia

DOI:

10.47709/brilliance.v4i1.4096

Keywords:

Data Mining, Electrical Panel, Linear Regression

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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

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ARTICLE Published HISTORY

Submitted Date: 2024-06-13
Accepted Date: 2024-06-25
Published Date: 2024-07-05

How to Cite

Susilo, A., Widodo , E., Rilvani, E., & Suryana, S. (2024). Linear Regression Algorithm Analysis for Predicting Electrical Panel Painting Quality. Brilliance: Research of Artificial Intelligence, 4(1), 286-293. https://doi.org/10.47709/brilliance.v4i1.4096

Issue

Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024

Section

Articles

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Copyright (c) 2024 Arif Susilo, Edy Widodo , Elkin Rilvani, Syahro Suryana

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.