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The rapid development of modern manufacturing technology has driven the emergence of human-robot collaboration (HRC) as part of the transformation toward a human-centric intelligent production system. In collaborative work environments, robots are not only required to work efficiently but also to interact safely and responsively with operators. However, most conventional industrial robot systems still use rigid motion controls and are unable to dynamically adapt to human activity around them.This research aims to develop a human-robot collaboration system by integrating computer vision technology to detect operator movement and applying adaptive control algorithms to the robot manipulator. The research methodology includes designing a collaborative workstation, implementing a computer vision-based motion detection system, developing an adaptive control algorithm, and evaluating system performance through various experimental scenarios. Evaluation parameters include task completion time, safe distance, and system response time.The results show that the developed system significantly improves the efficiency and safety of human-robot interaction compared to conventional systems, with shorter task times, optimal safe distances, and faster system response to operator movements.

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.

Natural fibers continue to attract the interest of researchers to develop them as composite reinforcements in automotive and non-construction building interior applications. Basically, natural fiber-reinforced polymer composites are not suitable for applications exposed to heat. Investigating changes in mechanical properties due to temperature increases, this research is very important to conduct. The addition of filler (CaCO3) is known to improve the performance of natural fiber-reinforced polymer composites. This study investigates the physical, mechanical, and thermal properties of polyester composites reinforced with waru fibers with CaCO3 powder filler. The composites were fabricated using the hand lay-up method with a volume fraction of 30% waru fibers and CaCO3 powder with a volume fraction of 0-10%. The density of the polyester composite increased from 1.42 to 1.68 and 1.87 g/cm3 as the volume fraction CaCO3 0-10%. The results of dynamic mechanical analysis (DMA) testing of the polyester composite showed that parameters such as loss modulus, storage modulus, and tan delta also increased with increasing CaCO3 content. Thermogravimetric analysis (TGA) testing also showed increased thermal resistance after the addition of 5% (wt) (STL) and 10% (wt) (ZMB) with a residual combustion of 6.54% and 7.89% for each STL and ZMB composite, respectively. Compared to the TKO composite, it had the lowest combustion residue of 3.61%. Tensile strength and elastic modulus showed the same trend, namely an increase, while the elongation of the composite decreased with the addition of CaCO3 powder. The overall test results showed that polyester composites reinforced with CWf fibers and CaCO3 fillers were suitable for automotive and building interior applications.

Decision Support Systems (DSS) can become inaccurate when used with imprecise, incomplete, or dynamically changing data. Fuzzy logic techniques based on conventional methodologies may be strong at handling vagueness, but are unable to adapt their behavior in response to different data distributions on their own. This paper recommends the creation of an Adaptive Fuzzy Logic Integration Framework that dynamically updates membership functions and rule weights in response to data variation to enhance decision accuracy under uncertainty. The described framework combines Fuzzy Inference Systems (FIS) with learning-based parameter update concepts borrowed from adaptive optimisation. The model was simulated and executed on a hybrid algorithmic platform that included gradient-based parameter tuning and iterative feedback learning. Experimental tests were conducted on uncertainty-generated data sets to compare adaptive and conventional fuzzy models in terms of ISME (Root Mean Square Error), convergence stability, and decision accuracy. Previous results show that the adaptive model achieves a 21.4% increase in accuracy and a 28% improvement in convergence rate compared to non-adaptive fuzzy systems. Moreover, the model ensures stable performance even in the presence of random data perturbations, demonstrating its ability to handle uncertainty. This book incorporates a self-tuning fuzzy decision model that converts static inference structures to dynamic evolving decision engines. The outcomes establish a foundation for next-generation smart DSS for real-time optimization in uncertainty.

Enhancing the efficiency of renewable energy on ships is crucial for reducing dependency on fossil fuels. This research employs the Research and Development (R&D) method, aiming to design and implement a solar panel optimization system for battery charging, with a focus on increasing power efficiency and providing real-time performance monitoring. The system is designed using Maximum Power Point Tracking (MPPT) technology to maximize the solar panel's power output. A 200Wp solar panel with dimensions of 1290 x 760 x 30 mm was utilized. Static testing results show that the deployed sensors possess a high degree of accuracy, with an average error of 0.71% for the temperature sensor and only 1.81% for the light sensor used to monitor environmental conditions. Dynamic and system integration tests prove that the MPPT implementation significantly increases power output efficiency by 30.83% compared to a system without MPPT. Furthermore, the system with MPPT charges the battery approximately 27% faster. Additionally, the developed Modbus protocol-based monitoring system enables comprehensive and remote monitoring of key parameters such as voltage, current, temperature, and light intensity via a cloud database. Data communication reliability tests confirmed the system's capability to transmit entire data packets to a Google Sheets database at a periodic interval of 15 seconds without failure. Based on these results, the developed solar panel optimization system is feasible for implementation in maritime environments to enhance the utilization efficiency of renewable energy and the operational reliability of onboard systems.

This study aims to analyze the effect of the combination of oil viscosity and shock load on the compression characteristics of telescopic shock absorbers on motorcycles, which include depth, time, and compression speed parameters. The experiment was carried out using two levels of oil viscosity, namely 10 cSt and 15 cSt (equivalent to SAE 10W and SAE 15W), as well as two shock load variations, namely 40 kg and 50 kg, which were dropped vertically from a height of 30 cm. Compression depth measurements were carried out using a slow-motion camera at a speed of 240 fps and the results were validated using Kinovea software. Meanwhile, the compression time measurement is carried out with a stopwatch, and the compression speed is calculated from the results of the comparison between the compression depth and the compression time. The experimental data were analyzed using Minitab software with the Two-Way ANOVA method to determine the influence of each variable on response. The results of the analysis showed that oil viscosity and shock load had a significant influence on the compression depth, while the viscosity of the oil also significantly affected the compression speed. However, neither the viscosity of the oil nor the shock load exerted a significant influence on the compression time. To support the results of the experiment, a mass-spring-damper system theory model approach was used, which represents the dynamic behavior of shock absorbers. This model helps to explain the compression response to changes in fluid and load parameters theoretically. Based on the analysis of experimental results and model simulations, the optimal combination that provides the best performance is found in the use of oil viscosity of 10 cSt (SAE 10W) with a shock load of 50 kg.

This report presents a comprehensive overview of the crucial role of Proportional-Integral-Derivative (PID) control systems in maintaining an optimal environment for premature neonates in modern infant incubators. The main focus is directed on the effectiveness of the PID system in regulating vital physiological parameters such as temperature, humidity, and stability of the baby's body temperature, which greatly determine the survival and development of the neonate. The PID control system is proven to be able to provide a fast and accurate response to changes in environmental conditions, thus maintaining parameter stability in real-time. The integration of PID technology with modern incubator devices enables precise automatic setup, supports energy efficiency, and improves patient safety and comfort. The report also discusses various PID tuning methods, such as Ziegler-Nichols and adaptive methods, which are used to improve the performance of the system in the face of environmental dynamics and individual characteristics of infants. Implementation challenges identified include system complexity, need for periodic calibration, and limited technical resources in healthcare facilities. However, continuous innovations in control design and algorithms have driven the evolution of incubator devices to be more intelligent and responsive. Thus, the PID control system plays a central role in supporting neonatal life-support technologies, while representing significant advances in biomedical engineering and intensive care of premature infants.

This research aims to develop and evaluate the performance of a steam plant prototype designed as an alternative source of electrical energy to support the Vessel to Grid (V2G) concept. Utilization of backup energy on ships is becoming important as electricity demand increases and demands for a more sustainable electrical system. This system relies on ESP32 microcontroller technology as a control center that functions to monitor and control several key parameters, including steam pressure, combustion temperature, boiler water level, and the generated electrical voltage. The research method used is an experiment with a static and dynamic testing approach. Static testing is carried out to measure the performance of main components such as the boiler, turbine, and generator separately, while dynamic testing focuses on evaluating the overall system by involving the integration of sensors and supporting actuators. The test data is then analyzed quantitatively to determine the system's response to variations in steam pressure, temperature, and other operational conditions. The results show that the steam produced by the boiler is able to rotate the turbine, thereby driving the generator to produce electricity. The maximum voltage achieved is 25.7 volts at a steam pressure of 50 psi. The highest energy conversion efficiency was recorded at 4%, while the lowest efficiency was 0.9%. These findings demonstrate that, despite its relatively low efficiency, the prototype can function as an alternative energy source and emergency backup solution. Thus, this research provides an initial contribution to supporting the implementation of the V2G concept through the development of a small-scale steam plant-based energy conversion system.

Stock returns represent a crucial parameter that serves as a reference for investors in evaluating company performance. A decline in returns has occurred in several mining companies listed on the IDX, despite the sector’s vital role in the national economy. This study aims to examine the effect of Corporate Social Responsibility (CSR), Return on Assets (ROA), Return on Equity (ROE), Debt to Equity Ratio (DER), and Firm Size on the stock returns of mining companies listed on the IDX during the 2022–2024 period. The sample was determined using purposive sampling, resulting in 56 observational data after outliers were removed. To meet the assumptions of classical tests, several variables were transformed using natural logarithms, and data were analyzed using multiple linear regression. The results indicate that CSR, ROE, and Firm Size have no significant effect on stock returns, whereas ROA and DER show a significant positive effect. These findings suggest that investors tend to emphasize financial fundamentals, particularly profitability and capital structure, rather than non-financial aspects such as CSR activities. The implication for companies is the need to enhance operational efficiency and optimize financial structures to attract investors and improve returns. Future researchers are encouraged to incorporate external variables such as global commodity prices, market risk, and macroeconomic indicators, as well as expand the observation period and apply more diverse methodological approaches to provide a more comprehensive understanding of stock return dynamics in the mining sector.

Reservoir heterogeneity has long been recognized as a critical factor influencing the efficiency of enhanced oil recovery (EOR) methods. Among the techniques applied, cyclic waterflooding is considered one of the promising approaches due to its relatively simple operational design and potential to improve sweep efficiency. This method involves alternating water injection in specific cycles to mobilize trapped oil and redistribute reservoir pressure. However, the variation in geological properties such as porosity, permeability, and fluid saturation creates challenges in achieving uniform displacement, especially in reservoirs with high heterogeneity. Understanding the role of heterogeneity is therefore crucial for optimizing cyclic waterflooding applications. This study applies a literature review approach by synthesizing findings from previous experimental and field studies that evaluated cyclic waterflooding under different reservoir conditions. The analysis compares the performance of cyclic water injection periods across reservoirs characterized by varying levels of heterogeneity. Parameters such as injection rate, water breakthrough time, and oil recovery factor were considered in evaluating the effectiveness of this method. The results highlight that reservoirs with high heterogeneity often experience uneven fluid distribution, leading to early water breakthrough and reduced oil recovery. In contrast, reservoirs with relatively low heterogeneity tend to respond better to cyclic waterflooding, resulting in improved sweep efficiency and higher incremental recovery. Moreover, the optimization of cycle timing and water injection intervals appears to significantly mitigate the negative effects of heterogeneity. In conclusion, the study emphasizes that reservoir heterogeneity plays a decisive role in determining the success of cyclic waterflooding. Tailoring injection strategies based on geological variability is essential to maximize recovery efficiency. Future research should focus on integrating advanced reservoir characterization techniques with adaptive cyclic flooding models to further enhance oil production outcomes.

Cassava tapai is a traditional Indonesian food product produced through the fermentation of cassava (Manihot esculenta) with the help of microorganisms, particularly the yeast Saccharomyces cerevisiae. This product not only has cultural and economic value but also contains bioactive compounds with health benefits, such as probiotics and fermentation metabolites. However, the quality of cassava tapai is significantly influenced by process factors, particularly incubation time. This study aimed to analyze microbial growth and changes in the chemical properties of cassava tapai with varying fermentation times. The study was conducted using an experimental design with fermentation times of 24, 48, and 72 hours at room temperature. The main parameters observed included the number of microbial colonies (cfu/g), pH changes, and alcohol content produced during the fermentation process. The results showed that microbial growth increased significantly, peaking at 48 hours, with the highest colony count compared to other treatments. After 72 hours, the number of colonies decreased, likely due to ethanol accumulation and decreased substrate availability, which reduced microbial activity. The pH value tended to decrease with increasing fermentation time, reflecting the formation of organic acids during the process. Meanwhile, the alcohol content showed an increasing trend from the beginning to the end of fermentation, although the growth rate was relatively slower at 72 hours. These findings confirm that varying incubation length significantly influences microbial dynamics and chemical changes in cassava tapai. The optimal fermentation time is around 48 hours, as this is the phase where the balance between microbial growth, alcohol formation, and sensory characteristics is maintained. The results of this study can serve as a basis for developing standards for cassava tapai production at both household and industrial scales, while also strengthening efforts to preserve traditional foods with a modern scientific approach.

Accurate battery modeling is crucial for the development of battery-based energy storage systems, especially for real-time control and energy management applications. This study proposes a dynamic parameter modeling approach for LiFePO₄ batteries using a first-order Thevenin equivalent circuit model. Parameter estimation is performed to obtain the internal battery parameters based on the Thevenin model, and the parameter dynamics are derived using the Euler numerical method to represent battery behavior during charging and discharging processes. Model validation is conducted by comparing the predicted terminal voltage with actual measurements using root mean square error (RMSE) and mean absolute error (MAE) as evaluation metrics. The results show that the model accurately captures the battery dynamics, with an RMSE of 0.233 and an MAE of 0.047. Therefore, the proposed model is suitable for real-world applications that require accurate and dynamic estimation of internal battery parameters.

Black Soldier Fly (BSF) larvae, or maggots, are a type of insect currently widely cultivated, primarily for animal feed. This is because BSF larvae contain essential nutrients such as fat and protein in high amounts, thus improving the nutritional quality of livestock that consume them. Therefore, the processing and preservation of maggots is crucial to maintain their nutritional content and extend their shelf life. One method used in maggot processing is drying. Drying aims to reduce the water content in the larvae, thereby preventing the growth of microorganisms that cause spoilage. One widely applied technique is drying using a microwave oven. However, before the actual process is carried out, simulations are often required to determine the distribution of heat and humidity. Simulation is one of the most effective ways to predict the drying performance of biological materials. This study used a simulation using the Computational Fluid Dynamics (CFD) method operated by Comsol Multiphysics 6.2 software. The parameters used in the simulation were an initial maggot temperature of 80°C, a drying time of 15 minutes, and a heat source of 1300 W/m³. Based on the simulation results, the final water content of the maggots was below 10%. Furthermore, the final relative humidity of the maggots ranged from 10–35%, while the final temperature of the larvae increased to 93–97°C. These results indicate that microwave drying can effectively reduce moisture content while maintaining the nutritional quality of BSF larvae. These simulation results can be used as a basis for practical maggot drying processes, thus supporting the production of efficient and nutritious animal feed.  

Improvement amount Distributed Denial of Service (DDoS) attacks in cloud infrastructure and edge computing demands solution adaptive, distributed, and efficient detection in a way computing. Research This propose an optimized Federated Learning (FL) based DDoS detection model using Centroid Opposition-Based Bacterial Colony Optimization (COBCO) to training the Elman Neural Network (ENN). The proposed architecture consists of of two components Main: on the edge node side, a hybrid Convolutional Neural Network–Gated Recurrent Unit (CNN–GRU) model is used to extraction feature local from traffic data network, while on the server side, model parameters from each node are collected and used for training an optimized ENN with COBCO. Approach This aim increase accuracy detection at a time maintain efficiency local data communication and privacy. In progress experimental, model tested use three benchmark datasets: NSL-KDD, CICIDS2017, and CICDDoS2019. The preprocessing process includes feature encoding categorical, normalization numeric, class balancing using SMOTE, as well as validation cross (k-fold). Initial results show that combination of FL, CNN–GRU, and COBCO–ENN produces improvement significant in accuracy and time convergence compared to approach conventional such as PSO, GA, and non- federative models. In addition, the proposed model capable maintain performance detection tall although executed in edge environment with limitations source Power.  Study This give contribution important in development system scalable, privacy-preserving, and adaptive intelligent DDoS detection to dynamics Then cross modern network. Integration of FL and COBCO in ENN training shows potential big for used in implementation real in cloud-edge infrastructure. In addition, the proposed model demonstrates strong scalability and adaptability, making it highly suitable for dynamic and evolving network environments.

Employee shift scheduling in the hospitality industry remains a critical yet complex task due to fluctuating operational demands, fairness requirements, and labour regulations. Many hotels still rely on manual scheduling methods, which are time-consuming and prone to biases, particularly in ensuring fair workload distribution across employees. Despite numerous studies on workforce scheduling, limited attention has been given to integer linear programming (ILP) models that address gender-based restrictions and operational fairness simultaneously in real-world hotel contexts, especially in developing regions such as Central Java. This study proposes an Integer Linear Programming (ILP) model to generate optimal shift schedules for hotel staff over a 31-day planning horizon. The model incorporates operational constraints, including one shift per day, gender-based restrictions (which prevent female staff from working night shifts), availability, minimum staffing levels, and fairness in workload distribution. Key parameters and binary decision variables were defined to ensure compliance with the hotel's specific requirements. Empirical data were collected from a hotel in Central Java involving 20 employees, and the model was implemented using Python with a Gurobi solver. The ILP model successfully generated optimal schedules in under 10 seconds, significantly outperforming the manual method, which required over 4 hours. While the manual schedule resulted in an imbalance where some employees worked over 27 days and others only 22, the ILP approach enforced a strict maximum of 26 working days for all staff. Furthermore, the fairness index (FI) improved from 19.2% in the manual method to 0% in the ILP-generated schedule, indicating complete equity in workload allocation. The proposed ILP model demonstrates its effectiveness in improving scheduling fairness, operational efficiency, and compliance with labour policies. This work not only addresses a critical research gap in hospitality scheduling practices in Indonesia but also offers a replicable framework for other labour-intensive service sectors. Future research may explore multi-objective extensions incorporating employee preferences, satisfaction, and dynamic demand fluctuations.

This research explores the effectiveness of heuristic techniques for solving combinatorial optimization problems, with a particular focus on the Traveling Salesman Problem (TSP). Combinatorial optimization is a critical area of study, especially in fields like computer science, engineering, and economics, where finding optimal solutions from a finite set of possibilities is crucial. However, the NP-hard nature of many combinatorial problems, such as the TSP, makes traditional exact methods like Branch-and-Bound and Dynamic Programming computationally expensive and inefficient for larger problem sizes. The primary objective of this research is to evaluate the performance of heuristic methods, including Simulated Annealing (SA), Genetic Algorithms (GA), and Iterative Computation techniques, such as Tabu Search (TS) and Particle Swarm Optimization (PSO). These methods are tested for their ability to provide approximate solutions efficiently. The findings reveal that while ACO provided the best solution quality, it had the longest runtime. TS was the fastest, though with slightly lower solution quality. SA and GA demonstrated a balance between solution quality and computational efficiency, but their performance heavily depended on parameter tuning. The hybridization of SA and GA showed potential for improving solution quality but introduced additional complexity. The research concludes that heuristic methods, especially when combined, offer viable solutions for large-scale combinatorial optimization problems, though the trade-off between solution quality and computational time must be considered when selecting an algorithm.

This study focuses on the development of an interactive web-based learning platform for Proportional-Integral-Derivative (PID) control systems, aimed at addressing the conceptual challenges faced by electrical engineering students when learning PID through conventional teaching methods. Despite its foundational role in control theory, PID remains difficult to grasp without practical visualization and hands-on experimentation. To bridge this gap, the research introduces a practical and accessible platform that enhances conceptual understanding through real-time simulations and physical interaction. The proposed system integrates key hardware components including an ESP-32 microcontroller, DC motor, rotary encoder, BTS 7960 motor driver, and I2C LCD. The platform’s web interface is built using HTML, Tailwind CSS, and JavaScript, enabling intuitive user interaction. Motor response data is captured via the ESP-32 and transmitted to the web interface using the WebSocket protocol, allowing users to instantly visualize system behavior as PID parameters (Kp, Ki, Kd) are adjusted. This dynamic feedback mechanism enables students to observe changes in system characteristics such as rise time, overshoot, and settling time in real time. To evaluate the platform’s feasibility, practicality, and educational effectiveness, beta testing was conducted among electrical engineering students using Likert-scale questionnaires. The results demonstrated that users were able to successfully interpret the impact of PID tuning on system performance. The average evaluation score reached 75.13%, indicating strong agreement regarding the platform’s educational value and its effectiveness in enhancing learning outcomes. In conclusion, the study affirms that the developed web-based platform offers a feasible, engaging, and pedagogically effective alternative to traditional learning approaches. By combining interactive simulations with physical experimentation, the platform significantly improves students’ understanding of PID control systems.

The digital transformation in education has encouraged the adoption of computer-based tests (CBT) using video content, which demands stable and efficient network performance. This study aims to evaluate the performance of two queue management algorithms, namely Random Early Detection (RED) and Per Connection Queue (PCQ), in maintaining the quality of service (QoS) of school networks during online video-based examinations. A case study approach was applied using a real network topology in a school environment, and QoS parameters such as throughput, delay, packet loss, and jitter were measured. The implementation was conducted using a MikroTik RB450Gx4 router configured with simple queue settings for each algorithm. The results show that PCQ provides more consistent performance under high user loads, achieving an average throughput of 56,482 bps and lower delay compared to RED. Conversely, RED performs better in scenarios with a small number of users. The study recommends using PCQ for networks with dynamic and dense user environments, while RED is more suitable for low-traffic conditions where latency stability is crucial. These findings offer practical guidance for managing bandwidth and improving the quality of CBT delivery in educational settings.

The Pulsed Nuclear Magnetic Resonance (PNMR) technique is a widely used spectroscopic approach for observing the relaxation phenomena of atomic nuclei in a magnetic field. In this technique, the spin-lattice (T1) and spin–spin (T2) relaxation times are key parameters, as they reflect the microscopic dynamics and structure of a material system. This article presents the results of a systematic literature review of fourteen primary references that discuss the fundamental concepts of NMR, PNMR techniques, the measurement of T1 and T2, and PNMR applications. Although NMR research has seen extensive development internationally, scientific literature in the Indonesian language that specifically addresses this topic remains scarce. Therefore, this paper is intended to serve as an initial systematic resource to introduce the basic principles and potential applications of PNMR in physics and materials science.

The increasing number of devices connected to the network and the rapid growth of data traffic have posed challenges in managing conventional networks. Traditional networks often experience inefficiencies in handling high traffic loads due to static traffic management, leading to congestion in some network paths while others remain underutilized. Software-Defined Networking (SDN) has emerged as an innovative solution by separating the control plane and data plane, enabling more flexible, centralized, and programmable network management. This study evaluates the performance of OpenFlow-based SDN using the ONOS Controller and compares it with conventional home Wi-Fi networks based on the TIPHON standard. The simulation was conducted using Virtual Machines (VMWare) and Mininet, which were connected to the ONOS Controller via the OpenFlow protocol, while the conventional network testing was performed using the Command Prompt. The testing included three data transmission scenarios: 1 host to 1 host, 2 hosts to 1 host, and 2 hosts to 2 hosts simultaneously, with measured parameters including delay and packet loss. The results indicate that in the 1 host to 1 host scenario, both SDN and conventional networks had a delay of less than 150 ms and 0% packet loss. However, in the 2 hosts to 2 hosts scenario, the conventional network experienced an increase in delay and packet loss of up to 17%, whereas SDN remained stable with a delay below 150 ms and 0% packet loss. This confirms SDN's superior efficiency in handling high network traffic through more dynamic traffic distribution. Additionally, this study confirms that SDN's centralized control allows for more adaptive traffic management, reduces congestion, and improves overall network stability. These research demonstrate SDN's efficiency in handling high traffic loads, making it a superior solution for modern network demands.