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The search and booking of boarding houses (kost) remain a significant challenge for newcomers in campus areas, particularly in Kelurahan Samata, home to UIN Alauddin Makassar, which admits approximately 15,000 new students annually. Many of these students come from outside the region and require temporary accommodation near the campus. This issue is primarily caused by limited access to information and the lack of an optimal system to support the search and booking process. Therefore, it is necessary to evaluate the level of user acceptance of the developed system. This study aims to analyze user acceptance of a boarding house search and booking information system in the case of Kelurahan Samata using the User Acceptance Testing (UAT) method. UAT is a testing approach used to determine whether a system meets user requirements, is accepted according to expected standards, and to identify the need for further improvements to ensure its feasibility for use. The UAT was conducted by end users, namely boarding house seekers and property owners/managers, by responding to 10 evaluation indicators: (1) ease of search, (2) time efficiency, (3) booking process, (4) geospatial search features, (5) completeness of information, (6) availability of suitable boarding houses, (7) accuracy of information, (8) ease of payment process, (9) check-in scheduling, and (10) navigation features for directions. The results of the User Acceptance Testing (UAT) indicate that the system achieved an average score of 87.77, suggesting that the system is highly acceptable and significantly facilitates users. These findings demonstrate that the system is well received by users, confirming that it functions effectively and is capable of accommodating all required functionalities.

The computer laboratory is an essential facility in higher education that requires efficient management of usage and environmental conditions to support the teaching and learning process. However, laboratory management at the Kalimantan Business and Technology Institute is still carried out manually, including scheduling, room condition monitoring, and report creation, which is prone to errors and time-consuming. This study aims to develop an Internet of Things (IoT)-based laboratory monitoring system prototype to improve the effectiveness of computer laboratory management. The approach used is Research and Development (R&D) with a prototype development model, allowing for design adjustments based on user feedback iteratively. Data were collected through observations, interviews, and document studies related to laboratory conditions and analyzed to determine the main system features, such as temperature and humidity monitoring, scheduling, and report generation. The results of the study show that the developed prototype can structure the laboratory workflow, provide real-time monitoring, facilitate schedule management, and simplify report preparation. This prototype is expected to serve as a foundation for developing a more comprehensive application, improving data accuracy, time efficiency, and the quality of laboratory management.

The computer laboratory is an essential facility in higher education that requires efficient management of usage and environmental conditions to support the teaching and learning process. However, laboratory management at the Kalimantan Business and Technology Institute is still carried out manually, including scheduling, room condition monitoring, and report creation, which is prone to errors and time-consuming. This study aims to develop an Internet of Things (IoT)-based laboratory monitoring system prototype to improve the effectiveness of computer laboratory management. The approach used is Research and Development (R&D) with a prototype development model, allowing for design adjustments based on user feedback iteratively. Data were collected through observations, interviews, and document studies related to laboratory conditions and analyzed to determine the main system features, such as temperature and humidity monitoring, scheduling, and report generation. The results of the study show that the developed prototype can structure the laboratory workflow, provide real-time monitoring, facilitate schedule management, and simplify report preparation. This prototype is expected to serve as a foundation for developing a more comprehensive application, improving data accuracy, time efficiency, and the quality of laboratory management.

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.

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.

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.

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.

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.

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.

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.

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.

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.  

This study aims to analyze the content planning process and strategy on the TikTok account @Garudasystrain.id managed by PT Garuda Systrain Interindo. Using a qualitative descriptive approach through observation, interviews, literature review, and active participation during six months of Field Work Practice (PKL), this study describes how the company structures its social media workflow to support educational communication in the field of Occupational Safety and Health (K3). The results show that the content planning process is carried out through systematic stages, starting from trend research, idea gathering, content planning sheet preparation, upload scheduling, approval process, and weekly collaboration between team members. These stages ensure that the resulting content is relevant, consistent, and in line with the company's educational objectives. In addition, the content strategy is structured based on the Hierarchy of Effects Model, which includes the stages of awareness, knowledge, liking, preference, conviction, and action, so that the message delivery flow can encourage the audience from recognition to action. The use of clear and targeted Call to Action (CTA) also strengthens interaction and encourages audience participation. Overall, this approach helps PT Garuda Systrain Interindo in using TikTok strategies, improving the company's image, and strengthening the effectiveness of digital communications related to K3.

This study analyzes the maintenance process of the Roll Bending machine used in the wind tower production line at PT Kenertec Power System, Cilegon, Indonesia. The Roll Bending machine plays a crucial role in shaping steel plates into cylindrical shell components, which are later assembled into wind tower sections. The objective of this research is to identify maintenance patterns, types of failures, and improvement strategies to ensure machine reliability and operational efficiency. The research employed observation, interviews with maintenance personnel, and documentation review to collect relevant data. The findings show that the machine experienced multiple failures, mostly related to hydraulic system leaks, PLC programming errors, and component wear such as cylinders, seals, and gear pumps. A significant increase in corrective maintenance activities occurred between August 2023 and April 2024, particularly in February 2024, indicating the need for a more consistent predictive maintenance strategy. The implications of this study highlight that optimized maintenance scheduling and monitoring are essential to reduce downtime, avoid production delays, and maintain product quality. This research is expected to support maintenance decision-making and contribute to the improvement of industrial machine reliability in wind tower manufacturing operations.

Frequent damage to the grinding disc components and the transmission system of the 15 kg/hour rice flour grinder is a problem faced. The purpose of scheduling component maintenance and repairs is to obtain predictions of maintenance and repair schedules and costs for the period 2026. The component maintenance and repair scheduling method includes examining previous period maintenance and repair data, applying the inspection-replace-repair-overhaul (IRRO) method, assessing component conditions, estimating component life, estimating technician costs, estimating supporting work equipment and supporting materials to be used in maintenance, estimating the time for replacing spare parts or reinstalling components after repair, estimating maintenance and repair costs in 2026, and calculating the maintenance cost to profit ratio. The results of component maintenance and repair scheduling show that the maintenance cost in 2026 is Rp 1,370,000,- with an estimated annual profit potential of Rp 21,600,000, and the maintenance cost to profit ratio is 6.3%, which implies that the 15 kg/hour rice flour grinder is still quite prospective and feasible to use for the next few years.

The problem with the sugarcane press machine is a decrease in performance in sugarcane pressing because it has been used since 2020 with an initial price of IDR 3,000,000, which is predicted to be IDR 5,500,000 in 2025 which occurs in the main components of the roler mill, V-belt, and bearings. The purpose of scheduling replacements and repairs is to obtain a schedule, estimated maintenance costs, and the ratio of replacement and repair costs to the profit of renting a sugarcane press for the period 2026. The scheduling method includes collecting data on the use of the sugarcane press machine, a list of replacements for 5 components for roler mills, V-belts, bearings, gasoline motor tanks, and engine frames from 34 components, component prices, estimated labor costs, equipment costs, supporting material costs, application of the inspection, replace, repair, and overhaul (IRRO) method, scheduling replacements and repairs, and calculating the ratio of replacement costs to profits. The results of the replacement and repair scheduling are in the form of maintenance costs of IDR 958,000, with a rate of IDR 10,000/hour for 588 hours/year, resulting in a profit of IDR 5,880,000, and a ratio of maintenance and repair costs to profit of 0.16%.

Problems in the meatball dough grinder are low production, increasing maintenance costs and disappointed meatball dough customers who receive orders late due to problems with the electric motor, Pulley, V belt, cutting knife. The purpose of the replacement scheduling is to obtain costs, maintenance-repair schedules in the period 2026, and the ratio of maintenance costs to profits. The replacement scheduling method includes collecting previous period maintenance data, applying the inspection-replace-repair-overhaul (IRRO) method, evaluating component working conditions, predicting component life, predicting repairman costs, predicting supporting equipment that will be used in maintenance, predicting the time to replace spare parts or reinstall components after repair, estimating maintenance and repair costs in 2026, and calculating the ratio of maintenance costs to profits. The results of the replacement scheduling show that the maintenance costs in 2026 are IDR 2,530,000 with an estimated rental rate for the meatball dough grinder of IDR 10,000/kg which has the potential to be rented for 1300 hours/year, and the ratio of maintenance costs to profits is 4.9% which implies that the meatball dough grinder with a capacity of 5 kg/hour still has the potential to sell well and is suitable for use in the coming years.

This study analyzes the optimization of creative content production on the TikTok platform of Garuda Systrain Interindo, a company engaged in occupational safety and health (K3) certification services. The research aims to (1) identify how the content production process is optimized and (2) examine how such optimization improves the effectiveness of creative content based on the principles of Integrated Marketing Communication (IMC). Using a qualitative descriptive approach, data were collected through observation of the company’s TikTok content, documentation, and analysis of content performance indicators. The results show that the optimization process includes structured planning through content planning sheets, determination of content pillars, talent briefing, consistency in editing style, and template standardization. These efforts are supported by post-production activities such as scheduling, performance monitoring, and evaluation through TikTok Analytics. Furthermore, the optimization significantly enhances content effectiveness, as indicated by increased engagement, reach, and stable performance of educational content. The implementation of IMC particularly in terms of message consistency, visual uniformity, and brand identity integration strengthens brand awareness and improves the clarity of K3 educational messages delivered to audiences. Overall, optimized and IMC-based content production contributes to more effective digital communication and strengthens the company’s professional image on social media.  

The increasing demand for cloud computing services has led to the rapid expansion of cloud data centers, which consume significant amounts of energy and contribute substantially to global CO2 emissions. As the IT industry grows, the environmental impact of these data centers becomes an urgent concern. Green Cloud Computing (GCC) has emerged as a solution to mitigate this impact by focusing on energy efficiency and reducing carbon footprints while maintaining the necessary functionality and performance of cloud infrastructures. However, traditional blockchain consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS) face limitations regarding energy consumption and scalability, which exacerbates the environmental burden. This study proposes a quantum-inspired blockchain consensus algorithm designed to optimize energy consumption and reduce latency in cloud data centers. By integrating quantum principles such as superposition and entanglement, the algorithm enhances task scheduling and resource utilization, enabling more energy-efficient operations without sacrificing performance. Simulations in a green cloud environment showed that the quantum-inspired algorithm resulted in up to a 30% reduction in energy usage compared to traditional consensus methods, with a 40% improvement in consensus processing time. These results suggest that quantum-inspired algorithms hold significant potential for enhancing the sustainability of cloud infrastructures by improving energy efficiency and scalability. Furthermore, this study discusses the feasibility of implementing quantum-inspired algorithms on classical hardware, addressing challenges in scalability and integration into existing blockchain frameworks. The findings provide valuable insights into the potential of quantum-inspired technologies to drive energy-efficient solutions in cloud computing.

The rapid advancement of intelligent systems has accelerated the adoption of data-driven solutions across diverse industries, creating an increasing need for models that are both efficient and privacy-preserving. While traditional centralized machine learning approaches offer strong predictive capabilities, they often struggle with challenges related to data privacy, network latency, and computational inefficiency-especially in distributed environments with heterogeneous devices. To address these limitations, recent research has explored hybrid learning frameworks that integrate federated learning, edge computing, and dynamic model optimization techniques. These hybrid approaches enable models to process and learn from data closer to the source while maintaining stringent privacy requirements by keeping raw data localized. Additionally, the incorporation of pruning strategies, adaptive model compression, or multimodal data fusion contributes to improved speed, scalability, and accuracy in real-time inference tasks. Such frameworks have demonstrated notable promise in settings characterized by high data volume, operational complexity, and the necessity for fast anomaly detection or decision-making. However, despite these advancements, several challenges remain, including synchronization delays across edge nodes, variability in hardware capabilities, and the need for more efficient aggregation algorithms. Future developments may involve leveraging next-generation pruning techniques, energy-aware edge scheduling, decentralized orchestration protocols, or the integration of digital twin technologies to further enhance performance. Overall, hybrid distributed learning frameworks represent an important evolution toward more intelligent, secure, and autonomous computational ecosystems capable of supporting the next wave of smart applications.