Binary Logistic Regression Analysis on Financial Performance of State-Owned Enterprises (Telkom and PLN): Case Study on NPM Change Based on ROA and TATO
DOI:
https://doi.org/10.38035/dijefa.v6i6.5775Keywords:
binary logistic regression, financial ratios, net profit margin, state-owned enterprises, financial performanceAbstract
This research investigates the effect of Return on Assets (ROA) and Total Asset Turnover (TATO) on the likelihood of Net Profit Margin (NPM) change in PT Telkom Indonesia (Persero) Tbk and PT Perusahaan Listrik Negara (Persero) over Semester II 2015 to Semester I 2024. Using binary logistic regression, the dependent variable, which increases NPM, is defined as a binary outcome (1 = increase, 0 = no increase), with ROA and TATO as independent variables. The analysis includes 34 valid cases from company financial reports. Regression results show ROA significantly predicts NPM growth (B = 0.609, p = 0.015), boosting the odds of increases, especially in Telkom’s asset-efficient operations. TATO is also significant (B = -0.213, p = 0.006), but its negative coefficient indicates that higher turnover may hinder NPM growth due to rising costs, particularly in PLN’s capital-intensive sector. The classification table reports 76.47% accuracy, with 73.68% correct predictions for non-increasing NPM (0) and 80% for increasing NPM (1). These findings reveal sector-specific patterns Telkom’s strength in asset utilization versus PLN’s operational cost challenges offering valuable insights for optimizing profitability and informing strategic financial decisions in the state-owned enterprises.
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