https://jurnal.ugm.ac.id/ijitee/issue/feedIJITEE (International Journal of Information Technology and Electrical Engineering)2018-04-24T11:34:53+07:00Risanuri Hidayatijitee.ft@ugm.ac.idOpen Journal SystemsInternational Journal of Information Technology and Electrical Engineeringhttps://jurnal.ugm.ac.id/ijitee/article/view/35025Analysis of Segmentation Parameters Effect towards Parallel Processing Time on Fuzzy C Means Algorithm2018-04-24T11:32:16+07:00Cepi Ramdanicepiramdani.ti14@mail.ugm.ac.idIndah Soesantiindahsoesanti@ugm.ac.idSunu Wibiramasunu@ugm.ac.idFuzzy C Means algorithm or FCM is one of many clustering algorithms that has better accuracy to solve problems related to segmentation. Its application is almost in every aspects of life and many disciplines of science. However, this algorithm has some shortcomings, one of them is the large amount of processing time consumption. This research conducted mainly to do an analysis about the effect of segmentation parameters towards processing time in sequential and parallel. The other goal is to reduce the processing time of segmentation process using parallel approach. Parallel processing applied on Nvidia GeForce GT540M GPU using CUDA v8.0 framework. The experiment conducted on natural RGB color image sized 256x256 and 512x512. The settings of segmentation parameter values were done as follows, weight in range (2-3), number of iteration (50-150), number of cluster (2-8), and error tolerance or epsilon (0.1 – 1e-06). The results obtained by this research as follows, parallel processing time is faster 4.5 times than sequential time with similarity level of image segmentations generated both of processing types is 100%. The influence of segmentation parameter values towards processing times in sequential and parallel can be concluded as follows, the greater value of weight parameter then the sequential processing time becomes short, however it has no effects on parallel processing time. For iteration and cluster parameters, the greater their values will make processing time consuming in sequential and parallel become large. Meanwhile the epsilon parameter has no effect or has an unpredictable tendency on both of processing time.2018-04-24T10:53:02+07:00Copyright (c) 2018 IJITEE (International Journal of Information Technology and Electrical Engineering)https://jurnal.ugm.ac.id/ijitee/article/view/35026Parameter Identification of Nonlinear System on Combustion Engine Based MVEM using PEM2018-04-24T11:32:16+07:00Trigas Badmiantotrigas.badmianto@mail.ugm.ac.idEka Firmansyaheka.firmansyah@ugm.ac.idAdha Imam Cahyadiadha.imam@ugm.ac.idIn four-stroke engine injection system, often called spark ignition (SI) engine, the air-fuel ratio (AFR) is taken from the measurement of lambda sensor in the exhaust. This sensor does not directly describe how much AFR in the combustion chamber due to the large transport delay. Therefore, the lambda sensor is used only as a feedback in AFR control "correction", not as the "main" control. The purpose of this research is to identify the parameters of the non-linear system in SI engines to produce AFR estimator. The AFR estimator is expected to be used as a feedback of the main "AFR" control system. The process of identifying the parameters using the Gauss-Newton method, due to its rapid computation to Achieve convergence, is based on prediction error minimization (PEM). The models of AFR estimator is an open-loop system without a universal exhaust gas oxygen (UEGO) sensors as feedback, called a virtual AFR sensor. The high price of UEGO sensors makes the virtual AFR sensor can be a practical solution to be applied in AFR control. The model in this research is based on the mean value engine models (MVEM) with some modifications. The research dataset was taken from a Hyundai Verna 2002 with the additional UEGO type of lambda sensors. The throttle opening angle (input) is played by stepping on the gas pedal and the signal to the injector (input) is set to a certain quantity to produce the AFR (output) value read by the UEGO sensor. This research produces an open loop estimator model or AFR virtual sensors with normalized root mean square error (NRMSE) = 0.06831 = 6.831%.Copyright (c) 2018 IJITEE (International Journal of Information Technology and Electrical Engineering)https://jurnal.ugm.ac.id/ijitee/article/view/35028Effect of Load Growth on PLTH Baron Techno Park Performance2018-04-24T11:32:16+07:00Mychael Gatriser Paemychel.pae@mail.ugm.ac.idTegar Prasetyotegar.prasetyo@mail.ugm.ac.idSuharyanto Suharyantosuharyanto@ugm.ac.idT. Haryonothr@ugm.ac.idRidwan Budi Prasetyoridwan.budi@bppt.go.idThe reliability of stand-alone and hybrid power plant systems was dependent on electrical loads that the system must supply. For example, on renewable energy sources (RES), Reviews of those systems needs to be calculated well before the development process. One of the most important processes in the initial calculation is the electrical load that must be supplied by the system. The electrical load has a major influence on the amount of power generating capacity. A power plant that has higher electricity production than the load to be fulfilled was considered capable of meeting the system electrical load requirements. However, in terms of the reliability, it is considered as a loss because it will affect the life of the components and the high cost of operating from the system. Therefore, this research discusses the effect of load growth on hybrid power plant system performance of Baron Techno Park. The result of the research shows that the total electricity production of Baron Techno Park hybrid power plant system is 319.695 kWh/year with Net Present Cost (NPC) is $560.077 and the cost of energy (COE) is $0.64/kWh. Total electricity consumption of the PLTH Baron Techno Park is 67.413 kWh/year with total excess electrical energy is 245,547 kWh/year. Load growth of 5%, 10%, 15%, and 20% of the total current load affect the consumption of electric energy, excess electrical energy, and COE. The higher the load growth will affect the total electricity consumption that is increasingly higher so that the total excess electrical energy is lower. This research found that the performance of the system is not influenced by load growth. The highest performance of the system is resulted by the wind turbine of 72.62%, followed by solar panels of 18.82%, and biodiesel of 8.56%.Copyright (c) 2018 IJITEE (International Journal of Information Technology and Electrical Engineering)https://jurnal.ugm.ac.id/ijitee/article/view/35030Variable Step Size P&O MPPT Algorithm on 250 W Interleaved Flyback Converter2018-04-24T11:34:53+07:00Y. Munandar K.munandar.sie14@mail.ugm.ac.idEka Firmansyaheka.firmansyah@ugm.ac.idSuharyanto Suharyantosuharyanto@ugm.ac.idMaximum power point tracking (MPPT) algorithm seek the MPP to maximize delivered the power of a photovoltaic panel. From several MPPT algorithms, the perturb and observe (P&O) algorithm is commonly used algorithm because of its easy implementation. However, it is not the most efficient algorithm due to the perturbation step is fixed. By using the high step size, the MPP tracking became fast but there would be a high steady state error and by using the low step size, there would be less steady state error but the MPP tracking became slow. Resulting in a waste of energy in steady-state conditions when the working point passes through the MPP and poorly dynamic performance indicated when the setpoint changes due to solar irradiation changes. In this paper, a modification variable step-size of the P&O algorithm has been proposed with setting the step-size automatically at each point of work. To validate the concept of modification variable step-size, simulation using PSIM has been carried out. Compared with the conventional P&O method with fixed step-size, the proposed modified P&O method can increase tracking speed and efficiency in the system.Copyright (c) 2018 IJITEE (International Journal of Information Technology and Electrical Engineering)