The productivity of oyster mushroom cultivation in low-lying areas are still not optimal. This is due to the cultivation of oyster mushrooms needs ideal temperature and humidity (temperature 22–28 ° C with a humidity of 60% – 80%), while nowadays temperature and humidity preservation process is done in a conventional manner. Given these problems, the researchers gave the solution by creating a tool that able to work automatically to monitor and control the temperature and humidity in oyster mushroom cultivation problem based on microcontroller. Inputs used in these system are the value of temperature and humidity data readings from DHT11. While the output of the system is two actuators, the first is the exhaust fan and the second is mist maker. In the operation of the appliance automatically there will be two choices of data processing methods are applied, the method of Fuzzy Logic Control (FLC) and Feed Forward Backpropagation Neural Network (BPNN). Performance tools based on the application of these two methods will be compared to determine the most optimal and effective method when it applied to the tool to automatically control temperature and humidity oyster mushroom farm house. Based on the test results and data analysis, the tools can work well and also perform that optimal and effective data processing method is Neural Network with an average conditioning response time of 69.8 seconds to reach the ideal temperature and 113.4 seconds for the ideal humidity.

Nowadays, the research in robotics field is growing. One of the studies in robotics is the control method of the robotic arm movement. In this research, a 3 DOF arm drawing robot was built. An inverse kinematic models of the robot arm is made using artificial neural network method. Artificial neural network model was implemented in a GUI application. The ANN model can work in real-time to control arm robot movement to reach certain coordinates. Based on test results, the inverse kinematic models of the arm drawing robot had an error rate under 2%. It is of 0.16% for X coordinate and 0.46% for Y coordinate.

Finding out about the damage of the induction motor particularly in the rotor bar is essential to ensure the continuity of the manufacturing process and to reduce the high cost of breakdown maintenance. In the past decades, the difficulty level to find fault in rotor bars has attracted many researcher to develop the solving method. Therefore, this work presents an alternative to discover fault on rotor bars based on analysis of stator currents (MCSA) using Discrete Fourier Transformation (DFT) and Extreme Learning Machine (ELM). In this study, ELM applied to detect abnormalities and fault of rotor bars on induction motor with no load condition. The performance of proposed algorithm would compared with Constructive Back Propagation Neural Network (CBP-NN). The structure of both methods consist of a single hidden layer with 20 neurons that activated by a tangent sigmoid function. In the experiment, the data input for all algorithms came from the normalized of DFT outcome. The test result shows that ELM is faster compared with CBP-NN about 0.0467 seconds in terms of time training. Although the accuracy of their training has a wide error deviation but for the new data pattern recognizing or its variation, ELM provides the output prediction closer to the target validation than CBP-NN. Thus ELM can be used as a top priority because of the high level of time efficiency and accurate predictions.

The use of the infrared (IR) forehead thermometer is a convenient way to measure the body temperature; however, it exhibits the concerns of accuracy, reliability, and repeatability because of improper measurement poses such as distance and angle to forehead, and other variations of subject and ambient conditions. According to the advantages of quick response and contactless measurement of using IR thermometry, this article additionally considers the factors of distance to IR sensors, subject’s facial skin color, and forehead pose, as well as ambient temperature and light intensity, to propose an accurate regression for body temperature estimation. As a consequence, the proposed multimodality sensor (MMS) system consists of a set of thermopile IR sensors, a distance sensor, a light intensity sensor, and a web camera. Furthermore, a principal component analysis (PCA) approach combining the artificial neural network is utilized for the body temperature regression. In order to reduce the measurement bias, the digital axillary temperature data was used as the ground body temperature for the machine learning training and validation. The MMS system is set randomly close to the forehead at a distance of 2–30 cm to emulate the human’s operation bias. We have totally 30 subjects for this study, and we divided them into four groups as: training dataset (14 subjects, IDs: 1–14 with 300 cases), validation dataset (three subjects, IDs: 15–17 with 64 cases), concurrent-time test dataset (three subjects, IDs: 18–20 with 64 cases), and three-months-elapsed test dataset (ten subject, IDs: 21–30 with 240 cases). In the training process, the $k$ -fold cross-validation technique ( $k = 5$ ) was applied to reduce the model overfitting. In addition, multiple linear regression (MLR) and stepwise regression (SR) models were also developed for performance comparison. The result shows that the PCA-ANN temperature estimation model outperformed other predictive models with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) values, and highest $R$ values and can optimize the low-cost MLX90614 sensor accuracy. The three-months-elapsed test group achieves MAE $0.1~^{\circ }\text{C}$ with a standard deviation of the error $0.03~^{\circ }\text{C}$ when compared to the state-of-the-art with low cost and contactless IR sensors.

This paper has proposed a prototype of a control system in deaerator storage tank level at Industrial Steam Power Plant based on artificial intelligence (AI). There are two kind methods of AI which are implemented in this research, first is Back Propagation Neural Network (BP-NN) and the second is Extreme Learning Machine (ELM). The proposed method is aimed to improve the performance of an existing Proportional Integral (PI) control method. The input variables are error level and load condition. The output variables are control valve percentage and indicator value. From the experiment, the result proved that ELM is fast superior to BP-NN according to the time of training process and error tolerance. Prototype-based on ELM is also working properly with an error tolerance of 0.15 %.

Renewable Energy became a popular topic at the beginning of the third millennium in order to seek an alternative energy and it is expected to be environmental friendly. This is caused by the consumed daily energy like coal, petroleum, natural gas and others are no longer exist and cannot be renewed. Wind energy is one of the energy which is easy to use and can be obtained through wind energy conversion system or it is usually called by wind turbine. A wind turbine should have an equal or close power coefficient (CP) value to the maximum wind turbine standard value. CP is the determining factor for the advisability of wind turbines, because the greater value of CP the wind energy conversion will also be greater. IPSO is a combination of Particle Swarm Optimize (PSO) technique with Incremental Social Learning (ISL). The addition of the ISL algorithm allows this method to obtain global optimum value faster because ISL is a method of adding particle at specific time based on existing information. In this research PSO and IPSO will be compared in order to find an optimum CP from wind turbine prototype. Comparison result with mathematical approach is produced MSE = 0.0258for PSO and 0.0222for IPSO.