Adhan Efendi

Control of fruit flies remains a major challenge in chili cultivation, as infestations can significantly reduce crop yield and quality. Conventional pest control methods are often ineffective, energy-dependent, or environmentally unsustainable. This study developed a solar-powered fruit fly trap that integrates bioengineering principles with renewable energy technology to provide a sustainable pest management solution. The trap was designed using Autodesk Inventor to produce detailed 2D and 3D models and fabricated from hollow iron through cutting, welding, and assembly processes. The electrical system consists of solar panels, a solar charge controller, a step-down converter, and a high-voltage module that powers the trapping mechanism. Bioengineering principles were incorporated by integrating a methyl eugenol attractant with a high-voltage electrocuting system, enabling selective attraction and effective capture of fruit flies while utilizing renewable solar energy as the primary power source. Preliminary field testing demonstrated that the prototype operated reliably under agricultural conditions and successfully captured fruit flies, indicating its technical feasibility as an environmentally friendly and energy-efficient pest management system for chili cultivation.

Elderly people represent a vulnerable population requiring continuous, reliable and timely health monitoring to maintain quality of life and reduce medical risks. Although existing wireless body sensor network (WBSN)‐based systems primarily focus on energy efficiency, limited attention has been given to optimising time efficiency and real‐time decision‐making performance. This study proposes an elderly health monitoring system based on WBSN using an actor‐critic deep reinforcement learning (ACDRL) framework to address this gap. The system utilises physiological state parameters, including heart rate, body temperature and oxygen saturation, to dynamically optimise monitoring and data transmission strategies. Device validation experiments demonstrated high sensing accuracy with a mean absolute percentage error (MAPE) of 0.68%. Model optimisation results indicate that a discount factor of 0.4 yields the best performance, achieving a mean absolute error (MAE) of 0.0401. Comparative evaluations against deep Q‐network (DQN)‐based, deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3) algorithms show that the proposed ACDRL model consistently outperforms existing approaches across mean absolute error (MAE), integral absolute error (IAE) and integral squared error (ISE) metrics. Furthermore, real‐world WBSN implementation involving multiple sensor nodes confirms that the proposed method significantly reduces time consumption, recording 1104 ms with 10 nodes lower than all benchmark models. These results demonstrate that the proposed ACDRL‐based WBSN framework provides a scientifically validated, time‐efficient and scalable solution for real‐time elderly health monitoring, contributing to the advancement of intelligent healthcare systems.

Detection and manipulation are critical functions in robotic arm technology within smart pharmacy environments, enabling accurate and efficient handling of pharmaceutical products. Despite rapid advancements, challenges persist in visual recognition under varying conditions, hardware–software integration, and real-time responsiveness. This systematic review, conducted using the PRISMA methodology, analyzes 20 selected articles from reputable databases such as MDPI, Wiley, ScienceDirect, IEEE Xplore, and others. The findings highlight the integration of advanced sensors such as force, torque, tactile, and RGB-D, and the application of deep learning models such as YOLO, CNN, and Mask R-CNN, which collectively improve object detection and manipulation accuracy. Reinforcement learning and classical AI planning further support adaptive automation. Future research directions include hybrid AI fuzzy logic models, edge computing for real-time data processing, digital twin simulations for predictive optimization, and micro–nano robotic technologies for precision drug handling. These innovations are expected to enhance the safety, reliability, and performance of robotic systems in pharmaceutical automation.

Background and Aims: The increasing elderly population presents significant challenges for healthcare systems, necessitating innovative solutions for continuous health monitoring. This study develops and validates an IoT‐based elderly monitoring system designed to enhance the quality of life for elderly people. The system features a robust Android‐based user interface integrated with the Firebase cloud platform, ensuring real‐time data collection and analysis. In addition, a supervised machine learning technology is implemented to conduct prediction task of the observed user whether in “stable” or “not stable” condition based on real‐time parameter.Methods: The system architecture adopts the IoT layer including physical layer, network layer, and application layer. Device validation is conducted by involving six participants to measure the real‐time data of heart‐rate, oxygen saturation, and body temperature, then analysed by mean average percentage error (MAPE) to define error rate. A comparative experiment is conducted to define the optimal supervised machine learning model to be deployed into the system by analysing evaluation metrics. Meanwhile, the user satisfaction aspect evaluated by the terms of usability, comfort, security, and effectiveness.Results: IoT‐based elderly health monitoring system has been constructed with a MAPE of 0.90% across the parameters: heart‐rate (1.68%), oxygen saturation (0.57%), and body temperature (0.44%). In machine learning experiment indicates XGBoost model has the optimal performance based on the evaluation metrics of accuracy and F1 score which generates 0.973 and 0.970, respectively. In user satisfaction aspect based on usability, comfort, security, and effectiveness achieving a high rating of 86.55%.Conclusion: This system offers practical applications for both elderly users and caregivers, enabling real‐time monitoring of health conditions. Future enhancements may include integration with artificial intelligence technologies such as machine learning and deep learning to predict health conditions from data patterns, further improving the system's capabilities and effectiveness in elderly care.

The learning material about body's defence system is considered difficult by students because it related to mechanisms internals organs and abstract concepts. The attempt that able to create is by increasing logical thinking and curiosity. Nevertheless, the lack of learning media used by teachers does not support the ability to think logically and curiosity of students. The purpose of this research is to create learning media based contextual teaching and learning (CTL) through electronic magazines that proper, practical, and effective to improving student's logical thinking and curiosity. The procedure used for the research are ADDIE development model consists of five steps, which are analysis, design, development, implementation, and evaluation. The trial test used a quasi experimental design of nonequivalent control group design. The control and experimental classes were selected using the random sampling method so that there were different numbers of students in each class selected. The trial subjects were divided into control class (35 students) and experimental class (36 students). Based on the results of data analysis revealed that: (1) CTL electronic magazines is proper to use as learning media with a very worthy category; (2) CTL electronic magazines is practical to use as learning media based on learner responses percentage of learning implementation ≥ 90% with very good category; (3) CTL electronic magazines are proven effective to increased the logical thinking ability and curiosity of students

Monitoring the health of the elderly using wearable sensors and Internet of Things (IoT) devices necessitates the collection of data from various sources, thereby increasing the volume of data to be gathered at the monitoring center. As previously elucidated, the system exhibits high accuracy in measuring variables such as blood oxygen saturation (BOS) and heart rate (HR), as well as demonstrating proficient accuracy in implementing fuzzy rules to detect the “stable” or “unstable” condition of elderly patients. Therefore, by integrating wearable sensors and IoT devices into the elderly health monitoring system, we can enhance the quality of monitoring and provide more timely healthcare solutions tailored to the elderly population’s needs. The integration of fuzzy rules and IoT technology into the elderly health monitoring system offers an efficient and effective solution for continuous health surveillance. In this research, we integrated fuzzy rules and IoT technology into the elderly health monitoring system using parameters such as BOS and HR, through the integration of MAX30100 sensors and ESP32 microprocessors. The implementation of fuzzy rules resulted in nine rules indicating whether an elderly patient is in a “stable” or “unstable” condition. The methodology involved (1) component preparation, (2) device accuracy analysis, (3) fuzzy rule development, (4) Android-based mobile application development, (5) fuzzy rule accuracy analysis, and (6) cost analysis. Experimental results indicated a device accuracy of 98.89% in measuring BOS and HR variables compared to medical devices and a fuzzy rule accuracy of 96% in detecting whether the patient’s condition is “stable” or “unstable.” Based on the experimental findings, the elderly health monitoring system provides a user-friendly, precise, and cost-effective solution to enhance the quality of life for the elderly population.

Stress can increase the heart rate, causing dangerous conditions that cause significant harm and even death. Therefore, managing stress well to control the heart rate is vital. Monitoring stress levels using biomedical devices is essential to prevent further damage to the individual. Regular monitoring facilitates the acquisition of individual stress levels through heart rate information. In this research, a MAX30100 sensor can produce Beats Per Minute (BPM) data, which show the stress experienced by a person during four daily activities (sitting, walking, running, and sleeping) and when watching a horror movie. This research underscores the potential of cost-effective wireless solutions to increase portability and facilitate real-time monitoring and remote surveillance. The researchers introduced an innovative methodology by reusing everyday consumer devices, initially intended for routine activities, to measure stress levels in a unique context—precisely in a controlled experiment involving a movie-watching scenario. The ultimate achievement of this research was creating a stationary heart rate monitoring device that is easy to access, easy to use, space-saving, and adept at effectively tracking stress indicators. BPM readings obtained from the MAX30100 sensor have scores similar to those obtained from the heart rate app. The researchers proposed the inclusion of other sensors, such as a photoplethysmograph, an electrocardiogram, and galvanic skin response, to provide a more holistic assessment of stress. Leveraging cloud technology for data storage enables long-term monitoring and in-depth analysis of stress patterns over time.

This research aims to develop educational and learning support facilities by developing teaching aids for instructors and practical training to enhance the capa This research aims to develop educational and learning support facilities by developing teaching aids for instructors and practical training to enhance the capa

Knowledge management system (KMS) is a method that can help companies manage knowledge assets within the company. A KMS in addition to being able to manage knowledge assets is also expected to be safe from unauthorized parties, easily accessible when needed and become a place for sharing knowledge for employees in the company. In its application, the documentation of knowledge which is the result of employee training at PT X still uses paper. So, when the company wants to implement knowledge sharing, it will be hampered because it is difficult to distribute paper documents to all employees. Based on this, the authors designed a website-based KMS. The KMS design in the modeling uses UML and the implementation uses the php and MySQL programming languages with the help of the Laravel framework and the SAW algorithm. This KMS is equipped with features that make it easier for the Company to implement knowledge sharing. The results of this KMS design, produce a system that has been tested using black box testing resulting in a 100% tested success percentage and user acceptance test testing that is tested on four assessment criteria with an average user satisfaction percentage of 82% and includes good criteria.