Abstract. A renewable energy is a new topic in education in Indonesia, especially for vocational education. The problem is the teaching trainer as a learning media is still not available at Universitas Negeri Malang while developing a trainer is expensive. This paper introduces a renewable-energy simulator, which can show the process of converting energy using hybrid solar cell and wind power systems. In case of the solar cell system, the simulator shows the process of converting the sunlight beam to energy production. Furthermore, the simulator shows the watt peak (WP) of the daily solar beam. In the case of the wind-power system, the simulator shows the capacity of power generator considering the size of rotor, wind speed, and the type of generator. The unique point of this simulator is that the hybrid systems of solar cell and wind power systems are demonstrated. While solar cell can't be effective if the sunlight beam is not available, it can be supported by the wind-power system, which is available for 24 hours, but it is depending on the speed of wind. Thus, implementation of this simulator can help students easier to understand and optimise the development of power generation using renewable energy.
Energy conservation, especially electricity, can be done by conducting an energy audit. Energy audit activities can analyse and find energy-saving opportunities from energy use. This study developed a prototype of a wireless electrical energy monitoring system on a laboratory scale to monitor the use of electrical energy from both electrical equipment using a microcontroller as a sensor node. These nodes have installed several sensors, namely the current sensor, humidity sensor, temperature sensor and light sensor. The protocol used in communication between nodes and servers is the HTTP protocol in the Internet of Things design that can communicate using internet network intermediaries. Data on the server can be monitored in real time using the application on the client side.
Genetic Network Programming (GNP) has been proposed as one of the evolutionary algorithms, which is represented by graph structures. It was extended to GNP with Reinforcement Learning (GNP-RL) which combines online learning and evolution. GNP-RL succeeded in implementing the wall following behaviors of a Khepera robot. The objective of this paper is to improve the robustness of GNP-RL by introducing fuzzy GNP with noises. Fuzzy GNP overcomes the sharp boundary problem using the probabilistic transition on fuzzy judgment nodes, which improves the exploration ability. Furthermore, the robustness of fuzzy GNP can be improved by adding Gaussian noises to the sensors during the training phase. In order to evaluate the robustness of fuzzy GNP with noises, the wall following of a Khepera robot is simulated. Simulation results show that fuzzy GNP with noises is superior to GNP-RL.
Coronavirus disease 19 (COVID19) is a disease caused by the new coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This disease has infected almost the entire world with a total of 47.5 million sufferers and a death toll of 1.2 million people so that WHO categorizes it as a global pandemic. The COVID19 case in Indonesia still shows an increasing trend even though various prevention efforts have been made. Proven efforts to reduce the spread of COVID19 include limiting physical interactions between humans or physical distance, maintaining the cleanliness of hands and limbs by washing with soap, and limiting outdoor activities by staying at home. Several government and private agencies have required employees to report their health conditions via web pages. Real-time and accurate mobile applications can help prevent the spread of COVID19. This research will develop a real-time monitoring and command system using mobile applications and cloud computing technology. The application will collect GPS-based location data, the number of people in the vicinity identified via Bluetooth, and the user's body condition in the form of temperature and oxygen levels in the blood. User data is stored and processed in a real time database in cloud computing which can be accessed through an application on the user's smartphone. The database also stores data on Covid19 sufferers and where they live. The application provides alerts when in a crowd and notifies the status of the region the user is in. Advice is given by the app when the recording of the body condition points to the early symptoms of COVID19.
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