Parental Income and Education as Predictors of Physics Exam Success: A Neural Network and Random Forest Regression Analysis - Mutia Rosiana Nita Putri; Kertanah; Mugni Bustari; Muhammad Aminuddin; Baiq Husnul Khotimah (Postgraduate, University of Mataram, 2025)
This study examines the relationship between the socioeconomic status (SES) of tenth-grade students at SMA Negeri 4 Praya and their final physics exam scores for the 2022/2023 academic year. SES indicators include parents' income and education level, collected via qualitative questionnaires and quantitative assessment of physics exam scores. Random Forest Regression and Neural Network techniques were used for analysis. The results showed no significant relationship between SES and physics scores. For parents' education level, Neural Network Regression yielded a Mean Squared Error (MSE) of 323.78 and an R² score of -0.0129, while Random Forest Regression produced an MSE of 327.08 and an R² score of -0.0232. Similarly, for parents' income, Random Forest Regression resulted in an MSE of 327.08 and an R² score of -0.0232, and Neural Network Regression yielded an MSE of 323.78 and an R² score of -0.0129. These negative R² scores indicate that SES does not significantly impact physics exam scores, highlighting the complexity of factors influencing academic performance. This research suggests that other variables may play a more critical role in determining students' success in physics. This research underscores the need for a more comprehensive approach to understanding and supporting student achievement in education.