The study was aimed to measure the performance of Fuzzy Logic Controller (FLC) on Line Follower Robot (LFR). FLC output is a deviation value of Pulse Width Modulation (PWM) to determine the rotational speed of the left and the right wheel. As input variables are current and previous line sensors. Tuning was applied to input and output variables in each membership function (MF) to conduct the best performance. This study used triangular membership function that consists of three MF. Mamdani Fuzzy Inference System (FIS) is used using nine rules. The result obtains that after MF tuning, the performance of the LFR settling time is 0.63s faster compare to that without tuning.
Nowadays digital imagery is used for many purposes. Starting just as a hobby of photography up to the purpose of security or identification. For more sensitive purposes, an image needs to be encrypted so that the image is not recognized by an unauthorized person. In this study, hybrid transposition is used to encrypt and decrypt RGB images. The hybrid transposition here involves the process of randomization and repositioning of pixels before transposition is made. The performance of the encryption is measured by the correlation coefficient where the good result is indicated by the correlation coefficient value close to 0 (zero). The smallest coefficient values obtained are -0.0227 for test images in the form of chessboard pieces that have almost the same black and white areas. The decryption process produces the exact same image as the original image, this is indicated by the MAE value equal to 0 (zero) and the correlation coefficient equal to 1.0.
The performance of an algorithm can be improved by using a parallel computing programming approach. In this study, the performance of bubble sort algorithm on various computer specifications has been applied. Experimental results have shown that parallel computing programming can save significant time performance by 61%-65% compared to serial computing programming.
Indonesia Energy Outlook (IEO) 2016 published by BPPT projected the electricity demand in 2025 significantly will increase more than twice to 513 TWh from 203 TWh in 2015. This projection is based on the target of 100% electrification ratio in 2025. Assuming an average population growth of 1.2% in 2025 and a nominal GDP growth of 5.02% in 2014 which are expected to increase to 8% in 2025.This study projected the total electricity demand for the period 2016-2025 based on GDP, population, and electricity sales per sector (household, commercial, and industry) from the period of 2000-2015. Time series data modeling using Auto Regressive (AR) model and Autoregressive model with exogenous input (ARX) implemented using Artificial Neural Network Back-Propagation (ANN-BP). The repetitive training method is used to achieve the specified target error.
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