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Academic productivity is a vital indicator of student success in higher education, but it is often hampered by the complexity of tasks, transitions in the learning environment, and digital distractions that trigger procrastination. This phenomenon demands a high degree of adaptability so that students do not become trapped in physical and mental exhaustion due to unmanaged workloads. Therefore, this study aims to analyze in depth the causal relationship between time management skills and academic productivity, as well as investigate their role in mitigating academic stress levels in students. The research method applied is a literature review with a qualitative-descriptive approach. The research data was sourced from secondary data in the form of 21 reputable scientific articles (national and international journals) published between 2020 and 2025. The data analysis process was carried out through the stages of data reduction, synthesis of findings, and narrative conclusion drawing to systematically map the relationship between variables. The results and discussion of the study show that time management has a significant positive correlation with improved learning achievement. Specific indicators such as daily schedule planning, priority setting, and self-regulation have been empirically proven to increase task completion efficiency and Grade Point Average (GPA) achievement. Conversely, poor time management was identified as a major predictor of cognitive overload and exhaustion, especially among students with dense curricula such as engineering majors. This study concludes that mastery of time management is not merely a scheduling tool, but a fundamental cognitive strategy that functions as a coping mechanism to maintain mental health and achieve an optimal study-life balance.

CPU scheduling is a core function in modern operating systems that significantly impacts system performance and efficiency. Among various scheduling algorithms, Priority Scheduling is widely used and exists in two main variants: non-preemptive and preemptive. The non-preemptive mode allows a process to run to completion, while the preemptive mode can interrupt a running process for a higher-priority one. Understanding the behavioral differences between these modes is crucial but often challenging through manual calculations. To address this, an interactive web-based application was developed to simulate and visualize both preemptive and non-preemptive Priority Scheduling algorithms. The research method involved designing the system logic based on the core principles of each scheduling variant, followed by implementation using standard web technologies: HTML, Tailwind CSS, and JavaScript. The application allows users to input custom process data or load predefined case studies, select the scheduling mode, and instantly receive a comprehensive analysis. The results include a dynamically generated Gantt chart, a detailed performance metrics table (including turnaround time and waiting time), and a step-by-step execution log. Through a comparative analysis of a specific case study, the application is proven to be an effective educational tool. It accurately simulates both modes and visually demonstrates the impact of preemption on execution order, resource utilization, and key performance metrics, thereby simplifying the learning process for students and educators.

This study aims to evaluate community satisfaction with the CSR Program of PT Pertamina Patra Niaga Fuel Terminal Samarinda focused on talent and interest development for children of the Butonese community in Sidodadi Urban Village. A quantitative approach was applied using the Community Satisfaction Index (IKM) survey instrument based on Ministerial Regulation PANRB No. 14 of 2017, supported by Importance Performance Analysis (IPA). Results indicate an IKM score of 84.18, which falls under the “very good” category. However, several priority attributes, such as scheduling accuracy and supporting infrastructure, require improvement. Recommendations include service quality enhancement, community engagement, and sustainability-based planning.

This study aims to analyze process management in the Linux operating system, focusing on how processes are managed from creation, scheduling, to termination. Linux utilizes various scheduling and memory management mechanisms to ensure system efficiency and stability. One of the schedulers used is the Completely Fair Scheduler (CFS), which provides a fair distribution of CPU time based on the priority and needs of the processes. This research also discusses the use of techniques such as paging for memory management and how Linux handles inter-process communication through pipes and shared memory. Experiments were conducted to measure CPU usage, memory consumption, and execution time by various processes with different priorities. The results show that Linux effectively manages resources, provides fair time-sharing, and maintains security by isolating the address space between the kernel and user-space. This study is expected to contribute to the further development of process management in Linux and provide insights for developers and system administrators to optimize operating system performance.

The management of production and operations in factory machinery is an optimal management effort in the use of resources consisting of labor, machinery, equipment and raw materials. The production and operations manager directs various inputs in order to produce various outputs or results in units of quantity, quality, price, specific time and location according to consumer demand. Efficiency and effectiveness of the production process to be achieved requires proper scheduling management so that the production output produced can be completed according to target. Management of production and customer orders is still simple, besides that the status of raw material inventories that cannot be directly known can often result in delays in the production process and even production cannot be done as soon as possible. Scheduling orders that enter using the system first in first out where the first order that comes is done, but has not considered the number of orders, priority of customers, maturity of workmanship and engine capacity so that the estimated completion of the order can not be known. To make it more efficient and directed, the Earliest Due Date method is used by sorting the production work based on the closest due date. This method can be used for scheduling on one machine or for scheduling on several machines (parallel machine) so that the scheduling management information system can manage effective production scheduling and produce production reports automatically as needed.