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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal of Electrical Engineering and Computer Science

Dwi Pebrianti

Universiti Malaysia Pahang

Author-ID : 2374668

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 5 Documents Claim Missing Document

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Articles

K-NN Classification of Brain Dominance Khairul Amrizal Abu Nawas; Mahfuzah Mustafa; Rosdiyana Samad; Dwi Pebrianti; Nor Rul Hasma Abdullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 4: August 2018
Publisher : Institute of Advanced Engineering and Science

The brain dominance is referred to right brain and left brain. The brain dominance can be observed with an Electroencephalogram (EEG) signal to identify different types of electrical pattern in the brain and will form the foundation of one’s personality. The objective of this project is to analyze brain dominance by using Wavelet analysis. The Wavelet analysis is done in 2-D Gabor Wavelet and the result of 2-D Gabor Wavelet is validated with an establish brain dominance questionnaire. Twenty-one samples from University Malaysia Pahang (UMP) student are required to answer the establish brain dominance questionnaire has been collected in this experiment. Then, brainwave signal will record using Emotiv device. The threshold value is used to remove the artifact and noise from data collected to acquire a smoother signal. Next, the Band-pass filter is applied to the signal to extract the sub-band frequency components from Delta, Theta, Alpha, and Beta. After that, it will extract the energy of the signal from image feature extraction process. Next the features were classified by using K-Nearest Neighbor (K-NN) in two ratios which 70:30 and 80:20 that are training set and testing set (training: testing). The ratio of 70:30 gave the highest percentage of 83% accuracy while a ratio of 80:20 gave 100% accuracy. The result shows that 2-D Gabor Wavelet was able to classify brain dominance with accuracy 83% to 100%.

Multi-Objective Evolutionary Programming for Static VAR Compensator (SVC) in Power System Considering Contingencies (N-m) Nor Rul Hasma Abdullah; Mahaletchumi A P Morgan; Mahfuzah Mustafa; Rosdiyana Samad; Dwi Pebrianti
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 9, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Static VAR Compensators (SVCs) is a Flexible Alternating Current Transmission System (FACTS) device that can control the power flow in transmission lines by injecting capacitive or inductive current components at the midpoint of interconnection line or in load areas. This device is capable of minimizing the overall system losses and concurrently improves the voltage stability. A line index, namely SVSI becomes indicator for the placement of SVC and the parameters of SVCs are tuned by using the multi-objective evolutionary programming technique, effectively able to control the power. The algorithm was tested on IEEE-30 Bus Reliability Test System (RTS). Comparative studies were conducted based on the performance of SVC in terms of their location and sizing for installations in power system.

Parameter Prediction for Lorenz Attractor by using Deep Neural Network Nurnajmin Qasrina Ann; Dwi Pebrianti; Mohammad Fadhil Abas; Luhur Bayuaji; Mohammad Syafrullah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 3: September 2020
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v8i3.1272

Nowadays, most modern deep learning models are based on artificial neural networks. This research presents Deep Neural Network to learn the database, which consists of high precision, a strange Lorenz attractor. Lorenz system is one of the simple chaotic systems, which is a nonlinear and characterized by an unstable dynamic behavior. The research aims to predict the parameter of a strange Lorenz attractor either yes or not. The primary method implemented in this paper is the Deep Neural Network by using Phyton Keras library. For the neural network, the different number of hidden layers are used to compare the accuracy of the system prediction. A set of data is used as the input of the neural network, while for the output part, the accuracy of prediction data is expected. As a result, the accuracy of the testing result shows that 100% correct prediction can be achieved when using the training data. Meanwhile, only 60% correct prediction is achieved for the new random data.

Intelligent Control for Visual Servoing System Dwi Pebrianti; Ong Ying Peh; Rosdiyana Samad; Mahfuzah Mustafa; N. R.H Abdullah; Luhur Bayuaji
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 1: April 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i1.pp72-79

This paper presents intelligent control for visual servoing system. The proposed system consists of a camera placed on a Pan Tilt Unit (PTU) which consists of two different servo motors. Camera and PTU are connected to a personal computer for the image processing and controlling purpose. Color threshold method is used for object tracking and recognition. Two different control methods, PID and Fuzzy Logic Control (FLC) are designed and the performances are compared through simulation. From the simulation result, the settling time of PID controller is 40 times faster than FLC. Additionally, the rise time of PID is about 20 times faster than FLC. However, the overshoot percentage of PID controller is 4 times higher than FLC. High overshoot value is not preferable in a control system, since it will cause the damage to the system. Real implementation of FLC on a home-built visual servoing system is conducted. Two different types of FLC, 9 and 11 rules of FLC are designed and implemented on the system. The experimental result shows that FLC with different total number of rules give different system performance. The settling time of FLC with 11 rules is 2 times faster than FLC with 9 rules. Additionally, the overshoot percentage of FLC with 11 rules is 2 times lower than FLC with 9 rules.

Mammography Image Segmentation: Chan-Vese Active Contour and Localised Active Contour Approach Mahfuzah Mustafa; Hana Najwa Omar Rashid; Nor Rul Hasma Abdullah; Rosdiyana Samad; Dwi Pebrianti
Indonesian Journal of Electrical Engineering and Computer Science Vol 5, No 3: March 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v5.i3.pp577-583

Breast cancer is one of the most common diseases diagnosed among female cancer patients. Early detection of breast cancer is needed to reduce the risk of fatality of this disease as no cure has been found yet for this illness. This research is conducted to improve the Gradient Vector Flow (GVF) Snake Active Contour segmentation technique in mammography segmentation. Segmentation of the mammogram image is done to segment lesions existence using Chan-Vese Active Contour and Localized Active Contour. Besides that, the effectiveness of these both methods are then compared and chosen to be the best method. Digital Database of Screening Mammograms (DDSM) is used for the purpose of screening. First, the images undergo pre-processing process using the Gaussian Low Pass Filter to remove unwanted noise. After that, contrast enhancement applied to the images. Segmentation of mammograms is then conducted by using Chan-Vese Active Contour and Localized Active Contour method. The result shows that Chan-Vese technique outperforms Localized Active Contour with 90% accuracy.

Co-Authors Hana Najwa Omar Rashid Khairul Amrizal Abu Nawas Luhur Bayuaji Mahaletchumi A P Morgan Mahfuzah Mustafa Mohammad Fadhil Abas Mohammad Syafrullah N. R.H Abdullah Nor Rul Hasma Abdullah Nurnajmin Qasrina Ann Ong Ying Peh Rosdiyana Samad

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