Article Per Year (5 Year)
p-Index From 2019 - 2024
0.817
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science ABDI SABHA (Jurnal Pengabdian kepada Masyarakat)
Nooritawati Md Tahir
Universiti Teknologi MARA
Religion Arts Humanities Computer Science & IT Education Electrical & Electronics Engineering Social Sciences

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
Learning Style Classification via EEG Sub-band Spectral Centroid Frequency Features Megat Syahirul Amin Megat Ali; Aisyah Hartini Jahidin; Nooritawati Md Tahir; Mohd Nasir Taib
International Journal of Electrical and Computer Engineering (IJECE) Vol 4, No 6: December 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (299.413 KB)

Abstract

Kolb’s Experiential Learning Theory postulates that in learning, knowledge is created by the learners’ ability to absorb and transform experience. Many studies have previously suggested that at rest, the brain emits signatures that can be associated with cognitive and behavioural patterns. Hence, the study attempts to characterise and classify learning styles from EEG using the spectral centroid frequency features. Initially, learning style of 68 university students has been assessed using Kolb’s Learning Style Inventory. Resting EEG is then recorded from the prefrontal cortex. Next, the EEG is pre-processed and filtered into alpha and theta sub-bands in which the spectral centroid frequencies are computed from the corresponding power spectral densities. The dataset is further enhanced to 160 samples via synthetic EEG. The obtained features are then used as input to the k-nearest neighbour classifier that is incorporated with k-fold cross-validation. Feature classification via k-nearest neighbour has attained five-fold mean training and testing accuracies of 100% and 97.5%, respectively. Hence, results show that the alpha and theta spectral centroid frequencies represent distinct and stable EEG signature to distinguish learning styles from the resting brain.DOI:http://dx.doi.org/10.11591/ijece.v4i6.6833
IQ Classification via Brainwave Features: Review on Artificial Intelligence Techniques Aisyah Hartini Jahidin; Mohd Nasir Taib; Nooritawati Md Tahir; Megat Syahirul Amin Megat Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 1: February 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (162.714 KB) | DOI: 10.11591/ijece.v5i1.pp84-91

Abstract

Intelligence study is one of keystone to distinguish individual differences in cognitive psychology. Conventional psychometric tests are limited in terms of assessment time, and existence of biasness issues. Apart from that, there is still lack in knowledge to classify IQ based on EEG signals and intelligent signal processing (ISP) technique. ISP purpose is to extract as much information as possible from signal and noise data using learning and/or other smart techniques. Therefore, as a first attempt in classifying IQ feature via scientific approach, it is important to identify a relevant technique with prominent paradigm that is suitable for this area of application. Thus, this article reviews several ISP approaches to provide consolidated source of information. This in particular focuses on prominent paradigm that suitable for pattern classification in biomedical area. The review leads to selection of ANN since it has been widely implemented for pattern classification in biomedical engineering.
Autism Spectrum Disorders Gait Identification Using Ground Reaction Forces Che Zawiyah Che Hasan; Rozita Jailani; Nooritawati Md Tahir; Rohilah Sahak
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 2: June 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i2.6143

Abstract

 Autism spectrum disorders (ASD) are a permanent neurodevelopmental disorder that can be identified during the first few years of life and are currently associated with the abnormal walking pattern. Earlier identification of this pervasive disorder could provide assistance in diagnosis and establish rapid quantitative clinical judgment. This paper presents an automated approach which can be applied to identify ASD gait patterns using three-dimensional (3D) ground reaction forces (GRF). The study involved classification of gait patterns of children with ASD and typical healthy children. The GRF data were obtained using two force plates during self-determined barefoot walking. Time-series parameterization techniques were applied to the GRF waveforms to extract the important gait features. The most dominant and correct features for characterizing ASD gait were selected using statistical between-group tests and stepwise discriminant analysis (SWDA). The selected features were grouped into two groups which served as two input datasets to the k-nearest neighbor (KNN) classifier. This study demonstrates that the 3D GRF gait features selected using SWDA are reliable to be used in the identification of ASD gait using KNN classifier with 83.33% performance accuracy. 
Non-linear behavior of root and stem diameter changes in monopodial orchid Mohd Khairi Nordin; Mohammad Farid Saaid; Nooritawati Md Tahir; Ahmad Ihsan Mohd Yassin; Megat Syahirul Amin Megat Ali
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3191

Abstract

Precision agriculture aims to maximize yield with optimum resources. Vast majority of natural systems are acknowledged as complex and non-linear. However, prior to formulation of precise models, linearity tests are performed to validate plant behavior. This study has presented proof that the water uptake system in monopodial orchid is indeed non-linear. The change in physical growth of root and stem due to temperature and relative humidity factors are observed. The work focused on Ascocenda Fuchs Harvest Moon x (V. Chaophraya x Boots) orchid hybrid. Three complementary methods are presented: linearity tests through 1) regression fitting; 2) scatter plots; and 3) cross-correlation function tests. Root diameter, stem diameter, temperature, and relative humidity are logged at 15 minutes interval for a duration of 71 days. The polynomial equations derived for root diameter and stem diameter changes attained strong regression coefficients. The non-linear behavior is further confirmed by the scatter plots where no linear associations are present between the independent and dependent variables. Subsequently, the cross-correlation function tests conducted on temperature-root diameter, temperature-stem diameter, relative humidity-root diameter, and relative humidity-stem diameter combinations also revealed weak correlation. Despite using different techniques, the behavior of physical changes has been consistently proven to be non-linear.
Review on anomalous gait behavior detection using machine learning algorithms Hana’ Abd Razak; M. Ahmed M. Saleh; Nooritawati Md Tahir
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (316.793 KB) | DOI: 10.11591/eei.v9i5.2255

Abstract

A review on anomalous behavior in crime by other researchers is discussed in this study that focused specifically on the linkage between anomalous behaviors. Next, comprehensive reviews related to gait recognition in utilizing machine learning algorithms for detection and recognition of anomalous behavior is elaborated too. The review begins with the conventional approach of gait recognition that includes feature extraction and classification using PCA, OLS, ANN, and SVM. Further, the review focused on utilization of deep learning namely CNN for anomalous gait behavior detection and transfer learning using pre-trained CNNs such as AlexNet, VGG, and a few more. To the extent of our knowledge, very few studies investigated and explored crime related anomalous behavior based on their gaits, hence this will be the next study that we will explore.
Human gait recognition using orthogonal least square as feature selection Rohilah Sahak; Nooritawati Md Tahir; Ahmad Ihsan Mohd Yassin; Fadhlan Hafizhelmi Kamaruzaman
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1355-1361

Abstract

This study investigates the potential gait features that are related to human recognition using orthogonal least square (OLS). Firstly, video of 30 subjects walking in oblique view was recorded using Kinect. Next, all 20 skeleton joints in 3D space were extracted and further selected using OLS. Additionally, SVM with linear, polynomial and radial basis function (RBF) kernel was used to classify the selected features. As consequences, OLS was proven to be able to identify the significant features using all three kernels of SVM since all recognition accuracy attained is higher as compared to the original gait features. Results attained showed that the highest recognition accuracy was 90.67% using 48 skeleton joint points for SVM with linear as kernel, followed by 46 skeleton joint points for SVM with RBF kernel namely 88.33% and accuracy of 86.33% for 38 skeleton joint points using  polynomial kernel.
Knowledge Improvement through Smartphone Utilization in the Remote Control System Module of Electronic Equipment at SMKS Muhammadiyah 9 Medan Abdullah; Maharani Putri; Moh. Zainul Haq; Arridina Susan Silitonga; Cholish; Suprianto; Nooritawati Md Tahir; Abdul Rahim Bin Ridzuan; Pius Fernando Hutauruk; Priansus Rhein Rumahorbo
ABDI SABHA (Jurnal Pengabdian kepada Masyarakat) Vol. 4 No. 3 (2023): Oktober
Publisher : CERED Indonesia Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53695/jas.v4i3.971

Abstract

Control technology is currently growing rapidly, one of the utilization of control technology that is currently experiencing rapid development is control of smart homes and automation systems coupled with rapid advances in the field of the Internet of Things, so that control technology becomes a remote control concept that is widely applied. Thematic Community Service Collaboration (TCSC) activity is a series of community service activities intended for the downstreaming of research products of Medan State Polytechnic lecturers in collaboration with Universiti Teknologi MARA (Malaysia) lecturers that can be utilized by the community (partner), namely SMKS Muhammadiyah 9 Medan aims to provide training from modules / trainers that have been specially designed consisting of several electronic equipment that has been equipped with wireless communication so that students can control electronic equipment remotely only through an application from a Smartphone without having to make direct contact like using a mechanical switch in general. The method used in this service is the lecture method followed by the discussion and question and answer method followed by the module / trainer demonstration method and how it works and the direct interaction method, namely students can try or practice directly, so that this training increases students' knowledge, skills and creativity towards the development of remote control technology in the future.
Co-Authors Abdul Rahim Bin Ridzuan Abdullah Ahmad Ihsan Mohd Yassin Aisyah Hartini Jahidin Arridina Susan Silitonga Che Zawiyah Che Hasan Cholish Fadhlan Hafizhelmi Kamaruzaman Hana’ Abd Razak Haq, Moh Zainul M. Ahmed M. Saleh Maharani Putri Megat Syahirul Amin Megat Ali Mohammad Farid Saaid Mohd Khairi Nordin Mohd Nasir Taib Pius Fernando Hutauruk Priansus Rhein Rumahorbo Rohilah Sahak Rohilah Sahak Rozita Jailani Suprianto