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Early detection of a potential heart attack is a crucial step in preventing sudden death from heart disease. This research aims to develop an Internet of Things (IoT)-based health monitoring system capable of measuring vital body data in real time and predicting the likelihood of a heart attack from CSV data obtained from sensors, integrated through RapidMiner as learning data using a machine learning algorithm, the Support Vector Machine (SVM). The system was built using an ESP32 microcontroller connected to a MAX30102 sensor to measure heart rate and finger oxygen levels (SpO₂), as well as a DHT22 sensor to measure temperature and humidity. The resulting data is sent to the Blynk application to display real-time data according to its parameters. The initial prediction logic was developed using a rule-based method based on medical thresholds for four vital parameters. The data was then used to train an SVM model as a classification system to detect potential heart attacks. Test results showed that the system can identify abnormal conditions with a good level of accuracy and provide early warnings based on changes in vital parameters in real time. This system is expected to be an initial solution for personal health monitoring, especially for individuals at risk of heart disease. It can be further developed with cloud integration and automatic notifications to users' devices.

Vital Signs Devices function to detect vital signs from the human body, such as body temperature, blood pressure, heart rate, and blood oxygen levels. These vital signs provide us information whether our body is healthy or not. This vital sign monitoring device used Arduino Node MCU ESP32 as the data processor integrated with a Wi-Fi module, thus, supporting Internet of Things (IoT) application system. It is connected to an Android smartphone and can display measurement data in real-time. There are three sensors used, namely skin temperature sensor, Nellcor Saturation Partial Oxygen (SPO2) sensor and NIBP sensor that can display parameters such as SPO2, NIBP, heart rate, and body temperature in real-time. Vital Sign Simulator was used to test the SPO2, NIBP and heart rate parameters. Body temperature parameter was tested using warmed water medium in a water bath. The results of normal SPO2, NIBP, heart rate, and body temperature measurement showed fairly small differences.

The rapid urbanization and industrialization of cities have significantly contributed to the rising pollution levels, especially in urban rivers, where water quality is often compromised. Monitoring water quality in real-time is essential for mitigating the adverse effects of water contamination. This research aims to design and implement an Internet of Things (IoT)-based system for real-time monitoring of water quality in urban rivers, focusing on the continuous collection and analysis of environmental data. The system utilizes a range of sensors to measure critical water quality parameters, including pH, temperature, dissolved oxygen (DO), turbidity, and various contaminants, all of which transmit data wirelessly to a central server for further processing. The study evaluates the accuracy, reliability, and efficiency of the IoT system in detecting water pollution and its ability to deliver real-time insights. Findings demonstrate that the IoT system offers a higher level of precision and faster detection compared to conventional monitoring methods, making it an effective tool for real-time pollution detection and decision-making. Additionally, the integration of the IoT system with a user-friendly visualization platform enhances the accessibility of the data for stakeholders, enabling them to monitor the water quality effectively. The study suggests that IoT-based water quality monitoring systems present a sustainable long-term solution for urban water management, offering cost and time savings. Moreover, the research highlights the importance of cross-sector collaboration to support the development and deployment of IoT technologies and recommends further advancements in sensor technologies to monitor additional water quality parameters.  

Stroke is the number one cause of disability and the second cause of death in the world. Blood flow that is not smooth in stroke patients causes hemodynamic disorders including changes in the value of oxygen saturation in the blood (SpO2) which can interfere with the function of internal organs including the heart due to lack of oxygen intake. So we need a system to monitor the oxygen saturation value which can be used as an early indicator in recognizing stroke patients. This system uses detection of two sensors to measure oxygen saturation in the blood, a microcontroller to process data, a monitor to display data, a buzzer as a warning of the lower limit of measurement and a micro sd card module to store measurement data that has been running. Parameters displayed include the date of measurement, patient ID, SpO2-1, SpO2-2, last SpO2-1 and last SpO2-2. This system will measure and display the SpO2 value of both right and left arms simultaneously. Stroke patients who experience muscle weakness in one hand may experience different values with a hand that does not experience muscle weakness. This study aims to develop an SpO2 monitoring system that will be used to detect stroke patients with non-invasive methods. The results show that the system has been developed and can be used to measure the patient's SpO2 from both hands simultaneously with a measurement error rate of ±2% for each sensor from standard medical equipment.

Background: Spinal anesthesia is a type of extensive nerve block by inserting a local anesthetic drug into the subarachnoid space at the lumbar level (Rehatta, 2019). This method produces anesthesia by blocking transmission, deactivating motor and sensory abilities, thus creating loss of pain sensation in the perineum. lower abdomen to lower extremities. Among the effects of spinal anesthesia that often occur, post-spinal anesthesia headache is a complication that often does not receive special attention. Due to the minor effects of spinal anesthesia. Management of headaches after spinal anesthesia, one of which is using non-pharmacological techniques with Head Up therapy, namely providing a 30 degree head elevation position with the aim of increasing blood flow to the brain in an effort to maximize oxygen flow to the brain which is believed to reduce the sensation of headaches. Objective: To analyze the effect of giving Head up therapy on headaches in post-operative patients with spinal anesthesia. Method: This research is a quantitative research using a type of experiment with a pre-experimental design, post-test control group design. Sampling technique: accidental sampling Population: The population of this study was 300 people. Sample: This study involved 76 respondents with a division of 38 respondents who were given Head Up therapy and 38 respondents who were not given Head Up therapy. Data analysis: Data were analyzed using the Mann Whitney test. The research instruments were Head Up SOP and Posttest Numeric Rating Scale (NRS) questionnaire. This research was carried out in February-April 2024 at Dr Hardjono Ponorogo Regional Hospital. Results: Based on research and after data analysis, the significance value (P value) in the Mann Whitney test was 0.001 (p>0.05). Conclusion: It was found that there was an effect of giving Head up therapy to post-operative patients with spinal anesthesia