The Effect Of Competence And Training On Employee Performance Through Job Satisfaction As An Intervening Variable At The Kenangan Coffee Shop In South Tangerang Area

Arie Pratomo Suryawardhana — 2026-04-05

This study aims to examine and analyze the effect of competence and training on employee performance through job satisfaction as an intervening variable at Kopi Kenangan coffee shops in the South Tangerang area. The independent variables in this study are competence (X1) and job training (X2), the intervening variable is job satisfaction (Z), and the dependent variable is employee performance (Y). Data were collected through the distribution of questionnaires to 50 employees working at various Kopi Kenangan outlets in the South Tangerang region. The research employed a quantitative approach with an associative research design, and data analysis was conducted using Structural Equation Modeling (SEM) based on Partial Least Squares (SmartPLS).

The results show that competence has a positive and significant effect on job satisfaction, with a path coefficient (original sample) of 0.505 and a P-value of 0.000 (< 0.05). Job training also has a positive and significant effect on job satisfaction, with a coefficient of 0.149 and a P-value of 0.043 (< 0.05). Furthermore, competence has a positive and significant effect on employee performance, with a coefficient of 0.182 and a P-value of 0.020 (< 0.05), while job training does not have a significant direct effect on employee performance, with a coefficient of 0.022 and a P-value of 0.829 (> 0.05). Job satisfaction has a positive and significant effect on employee performance, with a coefficient of 0.711 and a P-value of 0.000 (< 0.05). The indirect effect analysis indicates that competence has a significant effect on employee performance through job satisfaction, with a mediation coefficient of 0.359 and a P-value of 0.006 (< 0.05), indicating that job satisfaction acts as a mediating variable. Meanwhile, job training through job satisfaction does not have a significant effect on employee performance, with a mediation coefficient of 0.106 and a P-value of 0.423 (> 0.05), although the indirect effect is greater than the direct effect.

Optimizing Random Forest Networks for Human Resource Skill Gap Analysis Based on Future Workforce Demands

Arya Surendra — 2026-04-10

The accelerating impact of artificial intelligence, automation, and digitalization has disrupted traditional labor markets and created urgent challenges in aligning workforce skills with future demands. This problem arises because existing forecasting methods, such as ARIMA or conventional machine learning approaches, often fail to capture the nonlinear and temporal complexity of skill dynamics, leading to inaccurate predictions of future shortages and surpluses. This study aims to develop a predictive framework that improves accuracy in skill gap analysis and provides actionable insights for workforce planning. The proposed model, named Optimized Random Forest Network (RFN), integrates heterogeneous data sources including online job postings, occupational taxonomies (O*NET), and macroeconomic indicators. The model incorporates temporal feature extraction, text embedding of job descriptions, and exogenous signal integration, combined with hyperparameter optimization and ensemble refinement to strengthen robustness. The results demonstrate that the optimized RFN outperforms baseline models such as standard Random Forest, Gradient Boosting, and ARIMA achieving superior performance in regression (sMAPE = 9.1%) and classification tasks (Macro-F1 = 0.82). Furthermore, the analysis highlights increasing demand for skills in data analytics, artificial intelligence, and green technologies, while routine-based roles show declining relevance. These findings offer valuable contributions for policymakers, industries, and educational institutions in designing adaptive strategies to bridge skill gaps and align human resources with future workforce demands.

PENANOMICS: International Journal of Economics

The Effect Of Competence And Training On Employee Performance Through Job Satisfaction As An Intervening Variable At The Kenangan Coffee Shop In South Tangerang Area

Optimizing Random Forest Networks for Human Resource Skill Gap Analysis Based on Future Workforce Demands