This study proposes an increase in the measurement of soil water content with sensor characterization that can be integrated with the internet of things. The main contribution of this work is the improvement in measurement accuracy compared to measurements using a moisture meter. This is achieved through an electromagnetic approach using a pair of transceiver coils as a sensor. Determination of water content in the soil is carried out through the formulation of an equation model that connects the measured voltage on the receiving coil with the mass of water contained. It is known that the use of the equation model in the test data results in better accuracy with an error of 2.03% - 17.43%, compared to measurements using a moisture measuring device with an error of 13.21% - 32%. This equation model that uses the electromagnetic method provides an alternative solution for determining the soil water for wider land use so that can be used for internet of things application.
Breast cancer is a serious disease and one of the most fatal diseases in the world. Statistics show that breast cancer is the second common cancer worldwide with around two million new cases per year. Some research has been done related to breast cancer, and with the advancements of technology, breast cancer can be detected earlier by using artificial intelligence or machine learning. There are popular machine learning algorithms that can be used to predict the existence or recurrence of breast disease, for example, k-Nearest Neighbor (kNN), Naïve Bayes, and Support Vector Machine (SVM). This study aims to check the prediction of breast cancer recurrence using those three algorithms using the dataset available at the University of California, Irvine (UCI). The result shows that the kNN algorithm gives the best result in terms of accuracy to predict breast cancer recurrence.
Air conditioner make electricity demand becomes higher over time. International Energy Agency (IEA) shows that electricity consumption for air conditioner will be the main trigger for the increase in world electricity demand in 2050. Higher electricity demand caused by inefficient usage of air conditioner due to human error factors. Human error that mostly happen is forget to turn off the air conditioner. This condition make air conditioner will be operate all day. This research is aim to reduce human error case that happened by making automated air conditioner controller and monitoring based on internet of things. This research use passive infrared sensor as an input to make sure air conditioner in the room is used or not and temperature sensor DHT 11 to make sure air conditioner operation. Internet of things technology is used to monitor the output from the system and control the device. Data test shows that the device works well. Air conditioner controller device works as the command and scenario that given. Error reading for temperature sensor is 0.29% and best configuration for infrared transmitter and passive infrared at radius 90°.
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