In this study, designing a health monitoring system with an Android-based Application of Health Detector (AHD) application. The data displayed is an input for multi-sensor readings from the detection of body health. From the detected health, it will provide a determination of the body's health condition, using the fuzzy mandani algorithm. The variables calculated were age, gender, heart rate, body temperature, systolic blood pressure, diastolic blood pressure, and blood oxygen levels. The stages of the fuzzy mamdani method in determining body health conditions include the formation of fuzzy sets, application of implications functions, and composition of rules. From the results of this study, it was found that the age factor affects health conditions. Older people tend to have indications of health conditions, only some of them have indicated health conditions, and almost all of them have healthy health conditions. The level of accuracy of the fuzzy mamdani method in this study was 85.18%. This is because in this study many variables are used which causes many rules to be made so that they are prone to errors.

In a health monitoring system, an Android-based Health Detector (AHD) application uses fuzzy Mamdani to detect health concerns. The data given can be used as an input for multi-sensor readings produced from physiological health detection. The body's total health is determined when the detected health is merged with the fuzzy Mamdani algorithm. Anger, apathy, and apathy were calculated as variables. The fuzzy Mamdani method to body health problems involves creating fuzzy sets, applying implications functions, and synthesizing rules. This study found that aging affects both men's and women's health. Persons over 65 are more likely than younger people to suffer health issues, even if only a minority of them have been diagnosed. The fuzzy Mamdani technique had a high accuracy rate of 93.33 percent in this study.