<![CDATA[Teacher Teacher job satisfaction is an important issue in Human Resource Management because it is associated with employee motivation, commitment, and organisational effectiveness. Although teacher job satisfaction has been widely linked to leadership, working conditions, and self-efficacy, teacher competence is more frequently examined as an antecedent of performance than as a direct predictor of job satisfaction. Empirical evidence remains limited, particularly among public elementary-school teachers working in densely populated urban education systems in Indonesia. This study therefore examined the relationship between teacher competence and teacher job satisfaction among 160 public elementary-school teachers in East Jakarta. A cross-sectional quantitative survey design was employed. Teacher competence was operationalised through pedagogical, professional, social, and personal dimensions, whereas teacher job satisfaction encompassed satisfaction with work, the work environment, and social and managerial support. The data were analysed using descriptive statistics, item analysis, reliability testing, classical assumption tests, Pearson correlation, and simple linear regression. The findings showed that teacher competence (M = 4.18) and teacher job satisfaction (M = 4.10) were both rated positively. The regression model indicated a strong and significant positive relationship between teacher competence and teacher job satisfaction (r = .782; standardised β = .782; R² = .612; adjusted R² = .610; p < .001). These results suggest that teacher competence accounted for 61.2% of the observed variance in job satisfaction. This study contributes to Human Resource Management in education by positioning teacher competence as a strategic personal and organisational resource associated with positive work attitudes. The findings support competence development as a managerial strategy, particularly through professional training, digital-pedagogical development, mentoring, and academic supervision. Future studies should employ multivariable statistical models by incorporating recognition, leadership, workload, compensation, and other organisational factors.]]>
