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This study aims to analyze the operational condition of the distribution transformer at substation PY094, PLN ULP Pringgabaya, with a primary focus on identifying and calculating the level of load imbalance on the consumer side. Data were collected through direct measurements of electrical parameters, including voltage and current in each phase, followed by a detailed analysis of energy losses. The measurement results indicated a significant load imbalance. In Feeder B, the average phase currents were recorded at 103.8 A for phase R, 130.2 A for phase S, and 90.4 A for phase T. Meanwhile, in Feeder D, the average phase currents were 47.4 A for phase R, 18 A for phase S, and 20.4 A for phase T. This imbalance caused notable power losses in the distribution system, with an estimated daily energy loss of 28.94 kWh, assuming the system operates 12 hours per day. To address this issue, load balancing simulations were carried out using ETAP software. The simulation involved redistributing load values across each phase in the two main feeders. Feeder B was simulated at 46.82% of the transformer’s full capacity, while Feeder D was simulated at 12.38% of the total 160 kVA capacity. The simulation results demonstrated that redistributing the load significantly reduced the current imbalance, thereby minimizing power losses and improving the operational efficiency of the distribution substation. Therefore, load balancing strategies are essential for enhancing energy efficiency and ensuring the reliability of electricity supply in distribution networks.

Fatigue is a critical issue in labour-intensive small industries, especially in traditional food production such as tofu manufacturing. This study aims to develop a fatigue classification model using a decision tree algorithm by integrating subjective assessments of the work system through the Macroergonomic Organizational Questionnaire Survey (MOQS) and objective physiological indicators, specifically Cardiovascular Load (CVL). The research was conducted in a tofu home industry located in Kalisari Village, Banyumas, Indonesia. Primary data were collected from 10 workers through MOQS questionnaires and heart rate measurements taken at rest and during work. CVL values were calculated and used as labels for classification into three categories: low, moderate, and high fatigue. Meanwhile, MOQS dimension scores (organization, job, personal, environment, and technology) were transformed into interval data and used as classification features. A decision tree model was built using the CART algorithm and visualized for interpretability. The results show that all workers experienced at least moderate fatigue, with 20% categorized as high fatigue. The decision tree revealed that the dimensions of organizational and personal factors were the most influential in predicting fatigue levels. The model provides a practical and interpretable tool to support decision-making in scheduling, workload balancing, and ergonomic interventions. This study demonstrates a novel approach to combining macroergonomic assessments and physiological data with machine learning for practical fatigue risk management in small-scale food production environments.

The optimization of thread management in operating systems is essential to improve the performance of multithreading-based applications. This study analyzes various techniques to enhance thread management efficiency, including thread pooling, dynamic scheduling, load balancing, and synchronization contention reduction. Experimental results indicate that thread pooling reduces overhead by reusing threads, while dynamic scheduling improves multitasking responsiveness by prioritizing urgent tasks. Load balancing ensures equitable workload distribution across processor cores, minimizing execution time. Furthermore, reducing synchronization contention using mechanisms like semaphores decreases thread waiting time, thereby boosting application performance. These optimizations enable modern operating systems to efficiently utilize multi-core architectures, improving speed, responsiveness, and user experience. This research highlights the importance of thread management optimization as a foundational step towards developing adaptive and scalable operating systems for future complex and data-intensive applications.