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This study explores the effectiveness of a time management intervention in reducing academic procrastination among first- and second-semester undergraduate students at several universities in Malang. A quasi-experimental method was employed involving two groups with a total of 20 participants: 10 students in the experimental group who received a time management workshop and 10 students in the control group. The research instrument was the Academic Procrastination Scale developed by Onwuegbuzie. The intervention was conducted over six sessions focusing on techniques such as the Pomodoro Technique, Eisenhower Matrix, distraction management, relaxation, reward systems, to-do lists, leisure time management, and weekly scheduling. Data were collected through questionnaires and analyzed using an independent samples t-test and the nonparametric Mann–Whitney test. Participants were selected using a random sampling technique, and the academic procrastination scale was used as the primary measurement instrument. The results indicate that the time management intervention was effective in reducing academic procrastination, suggesting important implications for the development of more effective educational strategies. This research is expected to contribute to the design of more effective intervention programs to increase the academic productivity of students in higher education.

The rapid development of information technology has provided significant opportunities to improve the efficiency of academic administration in schools. One of the common problems faced by educational institutions is the manual process of subject scheduling, which is time-consuming and prone to schedule conflicts among teachers, classes, and learning time. This problem is also experienced by SMPK Flos Carmeli, where the preparation of subject schedules has not yet been supported by an integrated computerized system. This study aims to design and develop a web-based subject scheduling application at SMPK Flos Carmeli using the Model–View–Controller (MVC) architecture. The research method employed is Research and Development (R&D) with the Waterfall software development model, which includes the stages of requirements analysis, system design, and application implementation. Data were collected through interviews, observations, and literature review to obtain system requirements that align with the school’s conditions. The application was developed using native PHP with the implementation of the MVC pattern to produce a structured, maintainable, and flexible system. The results show that the developed application is able to support the subject scheduling process in a faster, more accurate, and well-organized manner. The system provides features for managing teacher data, class data, time slots, schedule arrangement, and schedule printing, thereby minimizing schedule conflicts and improving the efficiency of school administrative work. Therefore, this subject scheduling application is expected to support the digitalization of academic administration and enhance the effectiveness of the teaching and learning process at SMPK Flos Carmeli.

This research is framed in the topic of the application of the Yanbu'a Method in Islamic Boarding Schools. The world of education is currently faced with various changes in aspects of life in society. This is due to the rapid advancement of knowledge and technology, as well as the impact of globalization that is occurring in the world. This has an impact on religious education, especially in the teaching of the Qur'an. Not a few Qur'an teachers teach how to read the Qur'an not in accordance with the tajwid or makhorijul huruf that has been taught by the Prophet Muhammad. In addition, there is one of the most prominent obstacles in the implementation of Qur'an reading education, namely in terms of the use of Qur'an teaching methods. This is very urgent to be studied because the application of inappropriate Qur'an teaching methods can result in fatal errors in the way of reading the Qur'an. research, guided by Max Weber's qualitative approach to Verstehen theory, concluded that the obstacles encountered in implementing the Yanbu'a Method were a lack of coordination between teachers, significant differences in student abilities, low student discipline in muroja'ah (religious study), and students' busy schedules outside of Quranic study hours. Optimization of the Yanbu'a Method at Tanwirul Qulub Islamic Boarding School includes teacher training and education, grouping students based on ability, strengthening evaluation and monitoring, providing learning facilities and media, consistent lesson scheduling, and increasing student motivation.

Manual management of sports field bookings is still widely practiced and often leads to scheduling conflicts, data recording errors, and low service efficiency. This study aimed to design and develop a desktop-based sports field booking application that automates the booking process and manages schedules in a structured manner. The research employed a system design and development method using an object-oriented programming (OOP) approach. Data were collected through direct observation of the booking process, interviews with field managers, and documentation of system requirements. The application was developed using the Python programming language with the PyQt5 framework for the graphical user interface and MySQL as the database management system. The results showed that the developed application is capable of managing field data, schedules, bookings, and user information in an integrated manner while reducing recording errors and minimizing scheduling conflicts. The application of OOP resulted in a modular, well-organized, and maintainable system structure. This application is expected to improve the efficiency and accuracy of sports field booking management and provide a practical solution for implementing a computerized booking system.

Project scheduling is an important element for shipyard companies to gain more profit. The most frequently used analysis is the Critical Path Method (CPM). However, in reality, planning using the CPM method is considered less efficient because it does not consider the productivity of each job in it and adds safety time which causes the project duration to become longer. In accordance with the above problems, a new method for planning project schedules is now being developed, namely Critical Chain Project Management (CCPM). CCPM is a project planning method that emphasizes the resources needed to carry out existing tasks in the project. This method is carried out by eliminating multitasking, student syndrome, Parkinson's law and providing a buffer at the end of the project. In this final project, a comparison of the duration of the results of applying the CCPM method with the Critical Path Method (CPM) method is carried out in a case study of ship repairs at PT. Galangan Kapal Madura. The initial project scheduling uses the traditional method in the form of a Gantt chart which is then broken down in more detail and completely with the relationship between activities in the form of CPM, and then will be compared with the duration of the results of CCPM scheduling which has eliminated multitasking, eliminated Safety time for each activity and provided a buffer in the work.

Punctuality is a crucial factor in ship repair projects because it directly affects operational efficiency, project costs, and the utilization rate of shipyard facilities. Delays in work completion can lead to increased costs, disruption of ship operational schedules, and decreased shipyard productivity. Therefore, this study aims to analyze and optimize the repair scheduling of Ship X at PT XYZ using the Critical Path Method (CPM). The research data were obtained from the actual project schedule compiled using Microsoft Project software, including the duration of each activity and the dependency relationships between tasks. The analysis results show that the project has one main critical path with a total duration of 74 hours. Most activities are on the critical path with a float value of 0 hours, while the Non-Destructive Test Penetrant Testing activity has a time slack of 1 hour. The application of the CPM method has proven effective in identifying critical activities, improving schedule control, and supporting managerial decision-making in optimizing ship repair projects.  

This study aims to examine the aligenment between class scheduling and the performance of the live daily prayers among student4s at UIN Walisongo Semarang. As an Islamic higher education institution, UIN Walisongo expected to integrate academic activities with the religious obligateions of its students. However, the intensity of class schedules and the dynamics of campus activities often have the potensial to conflict with prayer times, making it necessary to investigate the exten to which academic schedules are synchronized with the required times for prayer. This research focuses on three main aspects: (1) the compatibility of class schedules with the times of the five daily prayers, (2) the extent to which academic administrator consider prayer times when designing class scheduls, and (3) the forms of taysir or facilitative measures provided by the anniversity to support timely prayer performance. Using a qualitative field research method and a case study approach, data are collected through observations, interviews, and documentation related to class schedules and worship facilities within the campus environment. The findings of this study are expected to provide a comprehensive understanding of the relationship between academic scheduling systems and the practice of performing the five daily prayers among students. In addition, this research aims to generate recommendations based on Islamic jurisprudence and Islamic time management principles, which my serve as a foundation for developing academic policies that are more supportive of religious observance. Such policies would help ensure that academic activities and religious obligations can operate in harmony and mutually reinforce one another.

This study examines the management of Kediri Nite Carnival (KNC), an annual event organized by the Kediri City government to commemorate the city's anniversary and showcase local arts, culture, and tourism potential. This research uses qualitative methodology and employs literature analysis, observation, and documentation as case study methods. The Miles and Huberman interactive model is used to analyze the data. The Kediri City Tourism Office has implemented each stage of event management according to Goldblatt's concept: research, design, preparation, coordination, and evaluation. The focus of the research was to determine the objectives and target audience; the design highlighted the local cultural theme “Exotic Flower in Paradise”; planning included budget planning, scheduling, and promotional strategies through social media and collaboration with local media; coordination was carried out through task distribution and cross-team communication; and evaluation assessed the success of the event based on community feedback and its effects on tourism and the local economy. The research results show that KNC plays an important role in improving the public's perception of Kediri City as a creative and cultured city. However, efforts to increase the number of tourist visits in the long term still need to be made. It is hoped that these findings will help the local government develop a sustainable tourism communication plan.

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.

The increasing demand for real time parallel processing in cloud computing environments necessitates the development of more efficient and fault-tolerant scheduling algorithms. Traditional scheduling methods, such as static algorithms, often fall short when handling dynamic workloads and system failures, leading to increased task latency and reduced system performance. In contrast, adaptive scheduling algorithms dynamically adjust to changes in system conditions and workloads, ensuring timely task completion and optimized resource utilization. This study evaluates the performance of adaptive scheduling algorithms in real time cloud environments, focusing on key factors such as task latency, system resilience, and fault tolerance. Simulation experiments were conducted using cloud computing models that incorporate fault injection scenarios, including network failures and virtual machine crashes. The results show that adaptive algorithms significantly outperform traditional static schedulers in terms of task latency reduction and improved system resilience. These algorithms demonstrated better fault recovery times and ensured consistent real time performance, even under failure conditions. The findings highlight the advantages of adaptive scheduling in cloud environments, particularly for applications requiring rapid data processing and high system reliability. Despite the promising results, challenges remain regarding the scalability and complexity of these algorithms in large-scale cloud systems. Further research is needed to optimize adaptive scheduling algorithms for efficiency, scalability, and comprehensive performance evaluation, taking into account factors such as energy consumption, cost, and reliability. This research contributes to advancing cloud computing infrastructures that can dynamically handle real time tasks and maintain high performance under varying workloads and failures.

This research explores the impact of Cache Aware optimizations on signal processing pipelines in High Throughput computing systems. The growing demand for efficient memory management in modern computing systems, especially for data-intensive applications such as artificial intelligence (AI) and multimedia processing, necessitates the development of optimized memory hierarchies. Traditional memory systems often suffer from memory bottlenecks, significantly reducing the performance of these systems. This study investigates how memory hierarchy optimizations, particularly cache line aware optimization, dependency-aware caching, and adaptive cache replacement algorithms, can mitigate these challenges and improve system performance. Through analytical modeling and experimental benchmarking, this work evaluates various memory hierarchy configurations, including processing-in-memory (PIM) and three-dimensional integrated circuits (3D ICs), comparing them to conventional systems. The results demonstrate that Cache Aware optimizations lead to a reduction in memory access latency by up to 30%, while throughput improved by up to 40%. Additionally, cache hit rates increased by 25%, and energy consumption was reduced by up to 20%, highlighting the effectiveness of optimized memory management. The research contributes to the field by providing valuable insights into the design and implementation of efficient signal processing pipelines. It also identifies key challenges, including the need for dynamic occupancy mechanisms and DAG-aware scheduling algorithms, and suggests potential areas for future research, such as the exploration of collaborative caching approaches and further optimization of cache-adaptive algorithms. This work lays the foundation for more efficient, high-performance computing systems that can handle large datasets and complex tasks in real-time applications.

Edge-Internet of Things (Edge IoT) systems are increasingly integral to applications that require real time signal processing, particularly where low latency and energy efficiency are critical. This paper explores the design and performance evaluation of a heterogeneous microprocessor architecture aimed at optimizing energy consumption and real time performance. The heterogeneous architecture integrates multiple types of cores, such as Central Processing Units (CPUs), Digital Signal Processors (DSPs), and Graphics Processing Units (GPUs), to allocate tasks based on computational demand. The proposed design significantly reduces energy consumption, particularly during high-performance tasks, while maintaining real time processing guarantees. Simulation-based performance evaluation was conducted to assess the energy efficiency, latency, and overall system performance under varying workloads, including real time Digital Signal Processing (DSP) benchmarks. The results showed that the heterogeneous architecture outperformed traditional homogeneous processors, demonstrating up to a 19-fold improvement in energy efficiency. Furthermore, the system reduced latency by up to 45% in real time applications, making it particularly suitable for Edge IoT environments such as industrial automation and smart healthcare, where both performance and energy efficiency are critical. Despite some trade-offs in task scheduling complexity, the heterogeneous design was able to balance power consumption and computational performance effectively. The findings suggest that this architecture can serve as a foundation for future Edge IoT systems, providing significant advantages in terms of energy efficiency, real time processing, and scalability. Future work will focus on further optimization of the architecture and exploring its application across various IoT environments.

The development of information technology requires service companies to improve the effectiveness and quality of their services, including in the field of electrical installation services. CV Givas Jaya Sentosa still faces problems in managing orders, customer data, and technician scheduling, which are done manually, resulting in inefficiency. This study aims to design and build a web-based electrical installation service provider information system using the Object Oriented Analysis and Design (OOAD) method. The research method used is qualitative with a research and development (R&D) approach, through data collection techniques such as observation, interviews, and documentation. The system is designed using Unified Modeling Language (UML) modeling and implemented as a web application. The results of the study show that the system is able to simplify the service ordering process, improve service and technician data management, and help monitor work status in an integrated and real-time manner. Thus, this information system can improve operational efficiency and service quality at CV Givas Jaya Sentosa.

The development and revitalization of the Sungai Asam drainage system in Jambi City face serious problems in the form of river sedimentation and limited channel capacity, which trigger flooding. These conditions necessitated a design change for bank protection from revetment to a retaining wall (Dinding Penahan Tanah/DPT) in the SA223 to SA228 segment. This study aims to analyze the construction cost and implementation duration of the DPT resulting from the design change. The theoretical framework covers retaining wall concepts, cost estimation (Bill of Quantities), and construction project scheduling. A descriptive-analytical method was applied using secondary data, including design drawings, work volumes, the 2025 Water Resources and Human Settlements Unit Price Analysis (AHSP), the 2025 Jambi Provincial Standard Unit Prices, and time analysis using the S-Curve and Critical Path Method (CPM). The results indicate that the total construction cost of the DPT in the SA223–SA228 segment is IDR 9.524.259.092,02, while the project duration is determined based on the critical path identified through CPM analysis. The discussion confirms that the design change significantly affects work complexity, construction costs, and schedule adjustments. The study concludes that systematic and measurable cost and time analyses are essential as a technical basis for decision-making in river revitalization projects to ensure effectiveness, efficiency, and the sustainability of flood control infrastructure.

This research investigates the development of integrated operational systems connecting terminal and ship operations for docking and berthing time optimization through systematic analysis of historical data. Port efficiency depends critically on minimizing vessel turnaround time, with berth allocation, docking procedures, and cargo operations coordination determining overall port productivity and competitiveness. Through qualitative analysis involving port operators, terminal managers, ship agents, harbor masters, and operations research specialists, this study examines how historical operational data can inform intelligent coordination systems improving berthing efficiency. Results demonstrate that data-driven integration systems incorporating predictive analytics, automated scheduling, and coordinated workflows can reduce average berth turnaround time by 15-30%, improve berth utilization by 20-35%, and decrease operational conflicts by 40-60% through optimized allocation and proactive coordination. Key implementation challenges include data quality and availability, system integration complexity, organizational coordination barriers, and resistance to automated decision support. Findings reveal that historical data-based optimization represents transformative advancement from experience-based scheduling to evidence-driven operational planning supporting port efficiency enhancement, capacity maximization, and service reliability improvement. This research contributes to port operations literature by providing practical frameworks for data-driven berthing optimization applicable to diverse port operational contexts.

This study analyses the synergy between teachers and parents through a home visit programme at Jenu Islamic Junior High School in supporting student learning assistance. A descriptive qualitative method was applied with data collection techniques through in-depth interviews, participatory observation, and documentation studies conducted over three months. The results show that the home visit programme, conducted regularly every three months, has succeeded in building effective collaboration between educators and parents in supporting student learning. Home visits not only strengthen the emotional bond between the school and families, but also enable teachers to understand the learning conditions of students directly in their home environment. This programme has had a significant impact on improving student motivation, attendance, and discipline. Despite facing obstacles such as scheduling difficulties and ethical challenges for female teachers in visiting the homes of male parents, the personalised approach applied in this programme proved to be capable of creating a more inclusive and sustainable educational environment. The home visit programme has become an effective mentoring model for improving the holistic academic and character development of students based on Islamic values.

This study aims to describe the application of management functions in pastoral care for PLHIV. The research method used is a descriptive qualitative method with data collection techniques through observation and interviews. The study was conducted at the HKBP AIDS Ministry (HAM) Balige. Research Findings indicate that the implementation of pastoral care management functions such as planning, organizing, implementing, monitoring and evaluation has been carried out well. The planning stage generally concerns the foundation's readiness to provide assistance in the form of data collection on the identity of the assisted, infrastructure, and scheduling. The organizing stage includes the division of personnel tasks, the formation of peer groups as service partners. The implementation stage is the aspect of implementing pastoral care with the aim of realizing assistance for PLHIV through early detection facilities, providing medication to PLHIV, and educating the community in the form of socialization or seminars. The monitoring stage is carried out by monitoring the progress of the assisted through ARV compliance forms. Meanwhile, evaluation of pastoral care for PLHIV is carried out in weekly meetings to ensure all activities are carried out properly. The implications of these findings support the principle of the importance of managerial aspects in pastoral care so that the assistance is more effective, structured and sustainable so that efforts to improve the quality of life and the healing process of PLHIV can be enhanced.

The problems encountered are damage to the rubber wheel mount and universal/cross joints on the 90 m/hour capacity wood profile making machine, which can affect the uniformity and speed of wood profile making. Maintenance and repair planning aims to be able to create a maintenance and repair schedule for the 90 m/hour capacity wood profile making machine for the period 2026, estimate maintenance costs and the ratio of maintenance and repair costs to machine profits. The maintenance planning method includes collecting maintenance data from previous maintenance periods, reviewing the specifications of the wood profile making machine, estimating the age and price of components that are estimated to be damaged, estimating the cost and duration of dismantling and installing components that have been repaired in accordance with the provisions of the requirements for usable components or replacement spare parts, scheduling maintenance and repairs, estimating maintenance and repair costs for the period 2026, and determining the ratio of maintenance costs to profits. The planning results in the form of a maintenance-repair schedule for the period 2026; maintenance and repair costs in 2026, the ratio of maintenance costs to profits, and their implications indicate that the machine is still prospective and usable.

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

The problem faced is the decline in the performance of a 650 liter/minute centrifugal pump due to wear on components, especially the impeller, rolling bearings, and mechanical seals in supplying process water and clean water in industrial production systems. The planning objective is to obtain a maintenance schedule for a 650 liter/minute centrifugal pump for the operational period of 2026 and the ratio between maintenance costs and profits generated by the machine. The maintenance planning method includes collecting maintenance data from previous maintenance periods, reviewing centrifugal pump specifications, applying the inspection, replace, repair, and overhaul (IRRO) approach, estimating the age and price of components that are expected to be damaged, estimating the cost and duration of dismantling and installing components that have been repaired in accordance with the provisions of the requirements for usable components or replacement parts, scheduling maintenance and repairs, estimating maintenance and repair costs for the 2026 period, and determining the ratio of maintenance costs to profits. The planning results are in the form of a maintenance schedule for the 2026 period worth IDR 4,290,000,-, a maintenance cost to profit ratio of 7.44% and the implications indicate that the machine is still suitable for use and prospective for operations in the next few years.