Prediction of the Impact of Bullying on Students Academic Achievement Using Linear Regression

Student learning achievement is a crucial element in the education sector, shaped by a variety of internal and external factors. Accurately predicting student achievement remains a significant challenge for educators and researchers, especially considering the psychological impacts of bullying. This study aims to construct a predictive model using linear regression to examine the influence of bullying levels, social support, and mental health on student achievement at SMPN 4 Pasarkemis, Tangerang Regency. A review of previous studies highlights the psychological toll bullying can have on academic performance, with many focusing on predictive models or statistical methods like linear regression to quantify these impacts. The model was developed using Python on the Google Colab platform, utilizing the pandas, statsmodels, and seaborn libraries for statistical analysis and visualization of variable relationships. Employing a quantitative, associative research design, the study involved 533 student respondents. The findings reveal that social support has the strongest positive influence on academic achievement, while higher levels of bullying and poorer mental health correlate with decreased performance. Notably, among the various forms of bullying analyzed, cyberbullying emerged as having the most significant negative impact on academic achievement. Although the model explains approximately 5.5 percent of the variation in student learning achievement, the majority of influencing factors lie beyond the scope of this analysis. The model offers potential for further development into a web-based predictive information system to assist educators in the early identification of students at academic risk.