The Pati Regency Transportation Service is a public-sector agency responsible for traffic management, public transportation services, transport-safety supervision, and the development of transport facilities and infrastructure. Achieving these mandates depends on employee performance. This paper examines the moderating role of the work environment in the links between work discipline, organizational commitment, and employee performance. Data were collected through a quantitative census survey and analyzed in SPSS using descriptive analysis and regression with interaction terms. The findings suggest that work discipline is associated with higher performance, organizational commitment does not show the anticipated positive contribution, and the work environment conditions both relationships.
This study aims to analyze the effects of tourist destination image and service standards on the desire to revisit through visitor satisfaction as a mediating variable at tourist attractions in Semarang City. This study uses a quantitative survey-based paradigm. Data was obtained by distributing questionnaires to 166 tourists who had visited tourist attractions in Semarang City using purposive sampling. Data processing was carried out using Partial Least Squares–Structural Equation Modeling (PLS-SEM) through the SmartPLS application. The findings of this study indicate that destination perception and service standards have a positive impact on visitor satisfaction. In addition, tourism image, service standards, and visitor satisfaction empirically have a positive effect on the intention to revisit. Mediation effect testing shows that visitor satisfaction successfully bridges the influence of place image and service standards on the intention to revisit. These findings indicate that the formation of a positive destination image and improvement of service standards play an important role in increasing tourist satisfaction and encouraging the intention to revisit. This study is expected to contribute practical recommendations for tourism image managers in designing strategies for sustainable tourism competitiveness development.
Leave is an employee right that plays a vital role in maintaining the balance between work productivity and human resource well-being in the workplace. Leave application patterns formed over time can reflect workload dynamics, organizational unit characteristics, and the effectiveness of operational planning. However, in organizations with large numbers of employees and high data volumes, analyzing leave application patterns is often suboptimal when relying on conventional relational database approaches. This study aims to analyze employee leave application patterns in Semarang City by applying a Big Data Analytics approach based on Apache Spark. The dataset used consists of over 150,000 leave application records from the 2020–2025 period, encompassing information on application timing, organizational units, and leave duration. The analysis process was conducted through extraction, transformation, and loading (ETL) stages using Apache Spark DataFrames, including transforming leave data into a daily basis and temporal aggregation. Furthermore, the K-Means algorithm was used to cluster leave application behavior patterns, while FP-Growth was applied to identify frequently occurring combinations of leave timing. The results indicate that most leave applications were made on weekdays with short durations; however, specific clusters showed a tendency to take leave adjacent to or sandwiching public holidays and weekends. These findings demonstrate the existence of recurring and structured leave behavior patterns, which can be utilized as a basis for evaluation and formulation of more effective and adaptive employee leave management policies.
Civil Servants are members of state institutions who have the duty to serve the public in a professional, honest, fair, and impartial manner in carrying out state, government, and development functions. that Civil Servants are regulated by a predetermined law that must be obeyed and carried out according to predetermined regulations. Public Servants are apparatus resources tasked with providing services to the community in an honest, fair, and equitable manner. The regulations that have been determined in accordance with the position or field that has been determined must be carried out in accordance with the policies and rules of law that apply. The purpose of the research conducted was to analyse and describe the effect of competence, job characteristics, and Organizational Citizenship on performance, with competence and job characteristics as mediators. The study population was 3,992 employees, with a sample of 100 employees. The main data was taken from primary data sources with a questionnaire method, and analysed by linear regression analysis. Based on the formulation of the problem and objectives presented in this study, associated with the research findings and discussion, several conclusions can be made several conclusions that competence and job characteristics affect OCB. Competence, job characteristics, and OCB affect employee performance in Tegal City Government. OCB is able to mediate the influence of competence and job characteristics on employee performance in Tegal City Government.
The garment industry requires accurate production output predictions to minimize operational inefficiencies and support adaptive production planning. However, static, manual production targets often fail to reflect the real-time dynamics of the production floor, typically resulting in overestimations. This study evaluates the accuracy, stability, and generalization capability of ensemble learning models via external validation on unseen operational data, while comparing their performance against traditional theoretical targets. Employing a quantitative experimental approach, the study utilizes 700 historical data points for model training and 60 recent observations as an external test dataset, incorporating manpower and the Standard Minute Value (SMV) gap as key variables. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination ( ). The results demonstrate that both models exhibit robust performance, achieving values above 0.90 on unseen data. Comparatively, Random Forest achieved an MAE of 6.008, RMSE of 8.56, MAPE of 23.29%, and an of 0.908, whereas Gradient Boosting yielded an MAE of 5.892, RMSE of 8.05, MAPE of 25.60%, and an of 0.919. Although Gradient Boosting outperformed Random Forest in absolute error metrics and , Random Forest demonstrated superior relative prediction stability, winning 51.67% of daily prediction comparisons in a "Battle of Models" framework. Visual analysis using scatter plots and box plots confirmed that Random Forest maintains a more consistent error distribution and captures actual output fluctuations more realistically than the company's manual targets. These findings indicate that integrating the SMV gap variable effectively captures hidden losses within the production process. Consequently, Random Forest is recommended as the foundation for developing a data-driven Decision Support System (DSS) to facilitate more adaptive, realistic, and efficient production target setting in the garment industry.
