The change of information and communication technology has brought many changes in daily life. The way humans interacting is changing. It is possible to express each form of communication directly and instantly. Social media has contributed data in size, diversity and capacity and quality. Based on it, the idea was to see and measure the tendency of depression and anxiety through social media using the Closed-Loop method using Facebook text mining posts. Through the stages of pre-processing including text extraction using the Naïve Bayes machine learning model for text classification, the early signs of depression and anxiety are measured using DASS-21 parameter. In total, 22,934 Facebook posts were contributed as training and learning data collected from July 2017 until July 2018. As a results, analysis and mapping of social demographics of users that are usually as a trigger of depression, and anxiety, such as grief, illness, household affairs, children education and others are available.
<span><span>Electronic health (E-health) uses information and communication technology including electronics, telecommunications, computers, and informatics to process various types of medical information, to carry out clinical services (diagnosis or therapy). Health is the most important asset in human life, therefore maintaining health is a top priority and serious attention needed. Heart rate and body temperature are vital signs that the hospital routinely checks for clinical signs and are useful for strengthening the diagnosis of a disease. In this research monitoring heart rate and body temperature with the wireless sensor network (WSN) method that uses <span>NodeMCU 1.0 as a controller module and wireless as communication between nodes, the wireless network used in this research Wi-Fi network. As a data taker, a DS18b20 temperature sensor and a heart rate sensor (pulse sensor) are needed, which will be displayed by the ThingSpeak web and smartphones. From the test results, the success rate of the system in detecting heart rates is 97.17%. Whereas in detecting body temperature the success rate of the system is 99.28%. For data transmission, the system can send data smoothly at a maximum distance of 15 meters with a barrier.</span></span></span>
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