PREDIKSI TINGKAT KEPARAHAN KLAIM KOMPENSASI K3 MENGGUNAKAN MULTIPLE LINEAR REGRESSION DENGAN KOREKSI HETEROSKEDASTISITAS: PREDICTION OF SEVERITY LEVEL OF OCCUPATIONAL SAFETY AND HEALTH (OSH) COMPENSATION CLAIMS USING MULTIPLE LINEAR REGRESSION WITH HETEROSCEDASTICITY CORRECTION - Aqid Fahri Hafin; Herman; Abdul Fadlil (LPPM Universitas Abdurrab, 2026)
The identification of determinants influencing the severity of occupational injuries (serious claims) is crucial for formulating precise and evidence-based Occupational Health and Safety (OHS) intervention strategies. This study aims to identify and quantify the most significant ergonomic risk factors and workplace hazard exposures affecting claim severity using 200 job-type observations from the O*NET-ANZSCO dataset published by Safe Work Australia. The analytical method employed is Multiple Linear Regression (MLR) with a staged validation approach. Since the initial regression model using the original data violated the assumptions of normality (Shapiro-Wilk < 0.001) and heteroskedasticity (Breusch-Pagan = 0.025), this study applies a Log-Linear model transformation and Robust Standard Errors (HC3) estimator to produce estimates that meet the Best Linear Unbiased Estimator (BLUE) criteria. The results indicate that the final model (Log-Lin HC3) is statistically significant simultaneously (Prob(F-statistic) = 0.0003) with an Adjusted R-squared of 0.801, meaning that 80.1% of the variation in claim severity can be explained by the model. Partially, four key risk factors are identified as significant: Exposed to Disease or Infections (p = 0.001), Spend Time Making Repetitive Motions (p = 0.006), Spend Time Bending or Twisting the Body (p = 0.008), and Exposed to Radiation (p = 0.035). These findings indicate that mitigating injury severity should prioritize these specific hazard exposures and ergonomic risk factors.