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.

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.

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.

Indonesia is a country with a tropical climate that has two seasons, namely the dry season and the rainy season. In the dry season, sun drying is the most widely used method for agricultural products. In the rainy season, people who open businesses in the food processing sector have difficulty predicting uncertain weather. The community needs solar thermal energy to dry food ingredients in the form of opak, tubers, fruits, etc. In the rainy season, the value of production has decreased. This study aims to create a drying machine that can help people to dry various types of food and fruit products. The research was carried out in several stages, namely field observation, literature study, design, manufacturing, and performance testing. The study states that: (1) the manufacturing process of electrical machine dryer was done in three stages: the initial stage, advanced stage, and the final stage. (2) the trial of an electrical dryer machine was used to dry opak and banana. Data obtained in the form of opak before drying weighs 3.4 ounces and after drying to 2.2 ounces. Whereas the banana before drying weighs 7.3 ounces and after drying to 4.5 ounces; (3) electrical dryer machine is very helpful in the process of drying food processed products in Subang Regency. Keywords: manufacturing, electrical dryer machine, opak and banana.

The application of Occupational Safety and Health (OSH) is essential in vocational institutions that have a high risk of accidents. Therefore, this study aims to evaluate the application of OSH in the Electrical Laboratory of Subang State Polytechnic using a qualitative descriptive method through observation and interviews. The results of this study indicate that aspects of the application of the standard Personal Protective Equipment (PPE) and the use of practical work standards have a score of 66.25% and 62.5%, respectively. Meanwhile, the application of room standards seen from the four main indicators, namely furniture, equipment, educational media, and supporting equipment obtained a total score of 91% (high feasibility). In conclusion, the application of the PPE standard and practical work standard must be considered in detail to improve the overall OSH score.