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Step-up converter is widely used to increase DC voltage level on PV systems either off-grid or grid connected. One of the step-up converters often used in PV systems is SEPIC converter. To improve its performance, many SEPIC converters have been modified. However, performance on various conditions has not been further investigated. In this study, the modified SEPIC converter was investigated under various change conditions for grid-connected PV applications. This converter was modelled and simulated using PSIM software. The modified SEPIC converter received input from PV array 15 kWp, and its output was connected to the three-phase inverter with grid and load. The irradiance level and ambient temperature were varied to test its performance and compared to Boost converter and SEPIC converter. For all tests, the performance of modified SEPIC converter was better than other step-up converters because it was able to rectify the quality of output voltage and more efficient.

In this experimental study, AR book app is used to improve student learning outcome of kindergarten in animal introduction subject. AR book app is an application based on Augmented Reality (AR) technology that adapts the kindergarten curriculum in Indonesia. AR book app has included 3D view and animal video. 3D based learning makes it easy for students to visualize learning materials and video based learning to makes students give attention when learning activity. In a field experiment at kindergarten, 111 kindergarten students was divided into two groups participated in learning activity that using different learning media. The two groups were group A and group B. Group A is an experimental group which taught using Augmented Reality (AR) book app. Group B is control group which taught using group note methods. Experimental result showed that students' performance in learning improved significantly by using Augmented Reality (AR) book app media. In this study, students indicated that the experimental group learning outcome is better than the control group.

The national sugar production capability especially of the state owned sugar factory tends to decrease while demand increases. To overcome the problem, government has issued the presidential Decree No. 5/2010 and declared a revitalization program of sugar industry as a priority development program. The Ministry of Industry has implemented the program by restructuring the sugar factory's machinery/equipments. Since there are many of factories (51 units) and the budget is limited, the ministry should be selective as to which factories need to be restructered, and therefore the program shoud be supported by prior information about the machinery efficiency status of each factory. To provide those information, this study applies the application of the Overall Equipment Effectiveness (OEE) method on 39 state owned sugar factories. The results indicate that the everage rate of OEE for the 39 sugar factories for the year of 2010 to 2012 is 61.28% (best practice: 77.4%), while Availability, Performance, and Quality is 86.43%, 91.39%, and 77.02% respectively. The study also identifies 19 worst factories in efficiency, and therefore recommends those factories as priority to be restructured.

<p>In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based on Arduino microcontroller is applied to the dynamic model of 5 DoF Robot Arm presented. MATLAB is used to detect colored objects based on image processing. Adaptive Neuro Fuzzy Inference System (ANFIS) method is a method for controlling robotic arm based on color detection of camera object and inverse kinematic model of trained data. Finally, the ANFIS algorithm is implemented in the robot arm to select objects and pick up red objects with good accuracy.</p>

This paper presents a Hopfield neural network (HNN) optimized fuzzy logic controller (FLC) for maximum power point tracking in photovoltaic (PV) systems. In the proposed method, HNN is utilized to automatically tune the FLC membership functions instead of adopting the trial-and-error approach. As in any fuzzy system, initial tuning parameters are extracted from expert knowledge using an improved model of a PV module under varying solar radiation, temperature, and load conditions. The linguistic variables for FLC are derived from, traditional perturbation and observation method. Simulation results showed that the proposed optimized FLC provides fast and accurate tracking of the PV maximum power point under varying operating conditions compared to that of the manually tuned FLC using trial and error.

The influence of social media in disseminating information, especially during the COVID-19 pandemic, can be observed with time interval, so that the probability of number of tweets discussed by netizens on social media can be observed. The nonhomogeneous Poisson process (NHPP) is a Poisson process with dependent on time parameters and the exponential distribution having unequal parameter values and, independently of each other. The probability of no accurence an event in the initial state is one and the probability of an event in initial state is zero. Using of non-homogeneous Poisson in this paper aims to predict and count the number of tweet posts with the keyword coronavirus, COVID-19 with set time intervals every day. Posting of tweets from one time each day to the next do not affect each other and the number of tweets is not the same. The dataset used in this study is crawling of COVID-19 tweets three times a day with duration of 20 minutes each crawled for 13 days or 39 time intervals. Result of this study obtained predictions and calculated for the probability of the number of tweets for the tendency of netizens to post on the situation of the COVID-19 pandemic.