<![CDATA[Human activity recognition technology allows a system to detect simple activities by humans, such as standing, sitting, lying, walking, running and others using a camera or sensor. The camera-based human activity recognition system has a lack of adaptability to light so that the accuracy obtained is not good, while wearable sensor-based systems that use multiple sensors cause discomfort when used and battery life problems. In this study a system can be made that can classify simple activities carried out by humans using the MPU6050 sensor which has an accelerometer and gyroscope sensor and uses the k-Nearest Neighbor classification method. Input from this system is the value of the accelerometer and gyroscope sensor readings sent using the NRF24L01 wireless communication module to Arduino Mega as a device that classifies and displays the classification results in Serial Monitor Arduino IDE. In this study the test was carried out using one sensor and two sensors. From the results of the tests performed, obtained the highest accuracy results of 93.75% for systems that use one sensor with sensor placement on the thighs and 96.25% for systems that use two sensors with sensor placement on the thighs and waist. For testing the computation time of the k-Nearest Neighbor method in classifying human activities, the average time taken was 173.6 milliseconds for classification using one sensor and 353.2 milliseconds for classification using two sensors.]]>
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