The hepatitis C virus (HCV) is considered a problem to the health of societies are the main. There are around 120-130 million or 3% of the world's total population infected with HCV. Without treatment, most major infectious acute evolve into chronic, followed by diseases liver, such as cirrhosis and cancer liver. The data parameters used in this study included albumin (ALB), bilirubin (BIL), choline esterase (CHE), -glutamyl-transferase (GGT), aspartate amino-transferase (AST), alanine amino-transferase (ALT), cholesterol (CHOL), creatinine (CREA), protein (PROT), and Alkaline phosphatase (ALP). This research proposes a methodology based on machine learning classification methods including k-nearest neighbors, naïve Bayes, neural network, and random forest. The aim of this study is to assess and evaluate the level of accuracy using the algorithm classification machine learning to detect the disease HCV. The result show that the accuracy of the method NN has a value of accuracy are high, namely at 95.12% compared to the method KNN, naïve Bayes and RF in a row amounted to 89.43%, 90.24%, and 94.31%.
Generally, Electronic Load Control (ELC) used in micro hydro power plant (MHPP) to controls the voltage between consumer load and a dummy load, still detects one parameter voltage or frequency generator only. Whereas in reality, any changes in the load on consumers, generator voltage and frequency also changed. When the consumer load down the electric current will be supplied to the dummy load, amounting to decrease in consumer load. When there is a transfer load, there will be distortion voltage and frequency, thus a special methods to reduce distortions by speeding up the process of transferring the electric load is needed. The proposed of this study is using fuzzy logic algorithm.To realize such a system, a comparison tool model of load control digital electronic fuzzy logic controller (FLC) and Proportional Integrator (PI) is required. This modeling using matlab program to simulate, the simulation result shows that the ELC based on fuzzy logic controller is better than conventional PI control, it seen from fast response to steady state condition. Keyword:Electronic load control Fuzzy logic Micro hydro power plant Proportional integral
<p>This paper aims to design and simulate an Electrical Permanent Magnet Generator (EPMG) for rural area wind power plant. The generators available in the market mostly are a kind of high speed induction generator which requires high rotational speed and an electricity to generate a magnetic field. In this project, a radial flux generator is designed to have a low speed rotation using permanent magnet type Neodymium Iron Boron (NdFeB). Software used for designing is Finite Element Method (FEM) Magnet software basis. The model also examined with Simulink/Matlab environment. Extensive modifications are applied to get optimum result by changing generator diameter, number of coils, the copper wire diameter, number of poles, and used slots. The simulation results obtained generator speed 500rpm, the average series voltage is 145 Vrms, the generator requires 18cm diameter, number of turn for each coil is 55, diameter of the copper wire used is 0.6mm, and number of poles is 8 pairs and 12 unit slots.</p>
Photovoltaics are becoming very popular, because the system does not produce pollution and can be installed anywhere, including in remote areas. However, in the use of photovoltaic found some common problems, it is difficult to get maximum and stable power. Therefore, to overcome these problems using Maximum Power Point Tracking method. On a photovoltaic system it is necessary to determine which converter will be used to increase the power output of the photovoltaic. The design of MPPT using gray wolf optimization algorithm that can track the output power quickly and reduce the oscillation in photovoltaic system. The result of the maximum power tracker using the gray wolf optimization algorithm is better than the incremental conductance algorithm of up to 0,4 s. Converters are used using soft - switching buck converter method to overcome the power losses that often arise in PV systems. The result of power output using soft-switching buck converter is greater than using buck converter. When comparing the efficiency of photovoltaic systems using the gray wolf optimization algorithm increases from using the incremental conductance algorithm.
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