Implementation of the Multiple Linear Regression Method to Predict Student Achievement Based on Social Status and Discipline

Authors

  • Reynaldo Saragih STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Indra Gunawan STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Iin Parlina STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

DOI:

https://doi.org/10.55123/jomlai.v2i2.3128

Keywords:

Linear Regression, Student achievement, Prediction, Social status, Discipline

Abstract

This research aims to implement the multiple linear regression method as an analytical tool to predict the academic performance level of students at SMA Kartika 1-4. The primary focus of the analysis will be placed on four critical predictor variables, namely parental income, discipline, attendance, and academic achievement. The multiple linear regression method is chosen because it can provide a robust statistical foundation for understanding the complex relationships between these variables and academic performance. Through the collection of data related to students' socio-economic status and their level of discipline, this research will build a multiple linear regression model to predict the level of student performance. The results of this research are expected to provide a more comprehensive understanding of the factors influencing students' performance in the environment of SMA Kartika 1-4. In-depth analysis of the relationships between parental income, discipline, attendance, and academic achievement can offer valuable contextual insights. This research is anticipated to provide a basis for the development of strategies or policies at the school level to improve student performance by paying specific attention to these aspects.

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Published

2023-06-30

How to Cite

Saragih, R., Gunawan, I., & Parlina, I. (2023). Implementation of the Multiple Linear Regression Method to Predict Student Achievement Based on Social Status and Discipline. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 2(2), 133–142. https://doi.org/10.55123/jomlai.v2i2.3128

Issue

Vol. 2 No. 2 (2023): June

Section

Articles

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Copyright (c) 2023 Reynaldo, Indra Gunawan, Iin Parlina

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