In 2018 Indonesia still ranked fifth as the country with the highest number of stunting in the world. A better level of a mother’s knowledge can decrease about 4% to 5% in the possibility of stunting in children. Efforts to increase maternal knowledge about the importance of preventing stunting in the first thousand days of life are not only the responsibility of the government, especially the Ministry of Health. The mass media are also responsible for providing knowledge to mothers. One of the most widely used media in accessing news and information is through mobile devices such as mobile phones. From this background the problem of this research can be formulated is how to build an Android-based mobile health application for monitoring and preventing stunting. The purpose of this research is to build an Android-based mobile health for stunting monitoring and prevention. The research method is Research and Development consists of two stages, the Research Phase using qualitative methods and the Development Phase using FAST system development methods. The research was conducted at the Poltekkes of the Ministry of Health Jakarta II, Department of Electromedical Engineering from January to December 2019. The results of the study are android mobile health applications for monitoring and evaluating stunting. From the result of system testing, the mobile health application for monitoring and evaluating stunting could work well.

Currently, medical experts use psychophysiological questionnaires to evaluate human stress levels during counseling or interviews. Typically, biochemical samples use urine, saliva, and blood samples to identify the effects of stress on the human body. This research explains that stress detection can be done by analyzing psychological signals and the importance of monitoring stress levels. The authors develop research on stress detection based on psychological signals. The system then processes the recorded data; the android application displays the calculation results. The database can also be accessed as a spreadsheet via a web application. The design of real-time stress detection and monitoring using internet of things (IoT) can work well.

Stress is a mental health problem that is hazardous if not recognized early. A promising approach for noninvasive stress detection involves leveraging camera technology; however, there are notable challenges involved in this method, particularly regarding signal accuracy and quality, which are primarily caused by motion artifacts. This study aims to improve stress classification accuracy by using web camera-based imaging photoplethysmography signals. We introduce a frame alignment method that can significantly correct noise to mitigate motion artifacts. We use heart rate (HR) and HR variability metrics to monitor stress and classify the measurements into stress and no-stress conditions. The sample in this study comprises students who were stressed using the arithmetic task method. Our findings showed that the mean HR and low-frequency component increased under stressful conditions while the high-frequency component decreased. The primary contribution of this study involves refining the accuracy and reducing the time required for stress classification time. Notably, the proposed approach markedly improves the accuracy, with substantial noise correction in the resultant signal. We evaluate various classification methods, including logistic regression, support vector machine, naïve Bayes, and random forest. Our results demonstrate that logistic regression achieved the highest accuracy of 98.5%, with a receiver operating curve value of 98%, an F1 score of 98.4%, and a computation time of 4.7 s. Overall, the proposed methodology holds considerable promise for camera-based noninvasive human stress assessment.

The heart is a vital organ that must always be kept healthy. Not only the heart, body temperature is also important to control the condition of the body with the environment and know the symptoms of serious illness.Heart rate and body temperature are very important parameters to determine a person's health condition. The importance of monitoring these two parameters so that we need gauges that are easy to use and carry so that they can always monitor the values of the two parameters within normal limits. The purpose of this research is to design and build an Android-based Heart Rate and Body Temperature Monitor.The methods used are literature study, tool design and interface, testing tools, and analyzing test results. Arduino Nano-based heart rate and body temperature detector uses Max 30100 sensor as a measurement of heart rate and DS18B20 sensor as body temperature sensor. The sensor readings will be processed by Arduino Nano which will be displayed on LCD 16 x 2 and Android.The test results show that the heart rate and body temperature monitoring tools function properly with an accuracy value of 99.1% for measuring heart rate and 99.4% for measuring body temperature. Suggestions as a consideration for developing this research next is to make the android display easier and have health information and what should be done by a system user related to heart rate and body temperature.

The regulation and supply of oxygen as one of the medical gases in the hospital is important to ensure the availability of these gases for the survival of patients. The regulation of oxygen gas in hospitals usually uses a piping system with manifolds. The manifold will monitor the oxygen gas pressure on each tube. Manifold systems that are widely used in general can only monitor pressure but cannot perform an automatic exchange on gas cylinders if the pressure is under the permissible conditions. The manifold system design developed is equipped with pressure monitoring for automatic exchange of oxygen gas cylinders using pressure sensors and microprocessors. The test results of the system using regulator and barometer comparisons showed the percentage value of sensor pressure accuracy of 96.92 percent and 97.16 percent. At pressure below the limit of 285 KPa manifold can perform the exchange of active gas cylinders automatically. These results show the manifold design built can work quite well.

Retinal vessel segmentation is part of the morphological extraction of retinal blood vessels that plays an essential role in medical image processing. Manual segmentation is possible to do, but it is time-consuming and requires special operators. Moreover, the possibility of variability between operators is vast. This study aims to answer the shortcomings of the manual segmentation process by automatically segmenting retinal blood vessels. The main contribution of this study is the use of a simple method to iteratively segment retinal blood vessels. All processes in the segmentation are simulated using Matlab. The algorithm was evaluated by comparing the results of the automatic segmentation with 20 manually segmented images from the STARE dataset. The result show specificity 98.13%, accuracy 93.60%, sensitivity 56.42%, precision 80.48%, and the dice coefficient 64.06%. In conclusion, the automatic retinal blood vessel image segmentation process worked well.