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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Norhaslinda Kamaruddin
Universiti Teknologi MARA
Computer Science & IT Electrical & Electronics Engineering

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

 1 
Pornography Addiction Detection based on Neurophysiological Computational Approach Norhaslinda Kamaruddin; Abdul Wahab Abdul Rahman; Dini Handiyani
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 1: April 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v10.i1.pp138-145

Abstract

The rise of Internet access, social media and availability of smart phones intensify the epidemic of pornography addiction especially among younger teenagers. Such scenario may offer many side effects to the individual such as alteration of the behavior, changes in moral value and rejection to normal community convention. Hence, it is imperative to detect pornography addiction as early as possible. In this paper, a method of using brain signal from frontal area captured using EEG is proposed to detect whether the participant may have porn addiction or otherwise. It acts as a complementary approach to common psychological questionnaire. Experimental results show that the addicted participants had low alpha waves activity in the frontal brain region compared to non-addicted participants. It can be observed using power spectra computed using Low Resolution Electromagnetic Tomography (LORETA). The theta band also show there is disparity between addicted and non-addicted. However, the distinction is not as obvious as alpha band. Subsequently, more work need to be conducted to further test the validity of the hypothesis. It is envisaged that with more participants and further investigation, the proposed method will be the initial step to groundbreaking way of understanding the way porn addiction affects the brain.
Detecting learning disabilities based on neuro-physiological interface of affect (NPIoA) Nurul Izzati Mat Razi; Abdul Wahab Abdul Rahman; Norhaslinda Kamaruddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 1: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i1.pp164-171

Abstract

Learning disability (LD) is a neurological processing disorder that causes impediment in processing and understanding information. LD is not only affecting academic performance but can also influence on relationship with family, friends and colleagues. Hence, it is important to detect the learning disabilities among children prior to the school year to avoid from anxiety, bully and other social problems. This research aims to implement the learning disabilities detection based on the emotions captured from electroencephalogram (EEG) to recognize the symptoms of Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and dyslexia in order to have early diagnosis and assisting the clinician evaluation.  The results show several symptoms that ASD children have low alpha power with the Alpha-Beta Test (ABT) power ratio and ASD U-shaped graph, ADHD children have high Theta-Beta Test (TBT) power ratio while Dyslexia have high Left-over-Right Theta (LRT) power ratio.  This can be concluded that the learning disabilities detection methods proposed in this study is applicable for ASD, ADHD and also Dyslexia diagnosis.
Neuro-physiological porn addiction detection using machine learning approach Norhaslinda Kamaruddin; Abdul Wahab; Yasmeen Rozaidi
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp964-971

Abstract

Pornography is a portrayal of sexual subject contents for the exclusive purpose of sexual arousal that can lead to addiction. The availability and easy accessibility of the Internet connectivity have created unprecedented opportunities for sexual education, learning, and growth for adolescences to be in the rise. Hence, the risk of porn addiction developed by teenagers has also increased due to highly prevalent porn consumption. To date, the only available means of detecting porn addiction is through questionnaire. However, while answering the questions, participants may suppress or exaggerate their answers because porn addiction is considered taboo in the community. Hence, the purpose of this project is to develop an engine with multiple classifiers to recognize porn addiction using electroencephalography (EEG) signals and to compare classifiers performance. In the experimental study, the neuro-physiological signals of EEG data were collected previously in Indonesia among students age 9 to 13 years old by researchers from the International Islamic University Malaysia (IIUM). The EEG data were pre-processed, and relevant features are extracted using Mel-Frequency Cepstral Coefficients (MFCC). Then, the features are classified to produce the outputs of valance and arousal. Subsequently, three different classifiers of Multilayer Perceptron (MLP), Naive Bayesian (NB), and Random Forest (RF) are employed to determine whether the participant is a porn addict or otherwise. The experimental results show that the MLP classifier yields slightly better accuracy compared to Naïve Bayes and Random Forest classifiers making the MLP classifier preferable for porn addiction recognition. Although this work is still at infancy stage, it is envisaged for the work to be expanded for comprehensive porn addiction recognition system so that early intervention and appropriate support can be given for the teenagers with pornography addiction problem.
Insights extraction on cross-cultural interaction through astronomy online labs using data analytics M. Bakri; Norhaslinda Kamaruddin; M. Hamiz; P. Marlia; A. H. S. Nurhasmiza; Z. Othman; N. A. S. Nilam
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp508-515

Abstract

Dialogical inquiry on astrology offers the participants to gain not only the intellectual and technological knowledge on the subject matter but also the social benefit of the interaction. Different cultures and values may pose as a challenge for adaptation when the participants start to collaborate in order to complete the group work. Hence, multiple sessions of cross-cultural interaction through Astronomy Online Labs had been conducted to give the participants a standardized platform to discuss and communicate. However, it is imperative to observe the content and frequency of the interaction to ensure both parties (Local and Non-local) benefited from such interaction. The interaction had been recorded and analysed to give us some insights for the improvement of the future participants’ engagement. The visualization techniques such as word cloud, word forest, timeline as well as Venn diagram approaches had been used and it is observed that the participants are actively communicating with the Non-local slightly dominating the session. It is hoped that the analysis tool can be embedded in the platform that it can provide dynamic analysis on the go while interaction happens so the moderator can steer the interaction to the intended topic.
Visualization of job availability based on text analytics localization approach Nur Azmina Mohamad Zamani; Norhaslinda Kamaruddin; Abdul Wahab; Nur Shahana Saat
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp744-751

Abstract

Rate of employment is a strong indicator of economic stability of a country. It relates to the number of volumes of produced products and services. If the unemployment rate is high, the amount of gross domestic product (GDP) of a country may be declined. One of the main factors that contributes to low rate of employment is the mismatch between job seeker and the requirement of the job applied.  This is due to the limited analysis performed on the relevant information on job advertisement; such as, skills, responsibilities of the job, location and expectation of the employers. The obscure job descriptions provided in the advertisement may result in application of unsuitable candidates that can cause rejection of the candidate and the potential employer may take a long time to filter and evaluate the applications. A system that is able to provide relevant information in a simple and catchy way is needed to simplify the task of job searching. In this paper we proposed a text analytics technique to extract users’ comments from social media such as Twitter and Facebook on job advertisement. The result is then displayed in a geotagged map that can reveal the density of job availability based on geographical location. The job seekers can easily observe and select their desired job location. The initial system shows potential of the inclusion of the proposed approach in job advertisement websites. In comparison to other job searching websites, this system can provide additional information on public view about the advertised job obtained from the social media text analytics. With this additional information, jobseekers have more confidence in job selection and allows employers to receive more suitable candidates for the available positions. It is hoped that the proposed system can tailor the job advertisements to the need of the jobseeker and making the job application more relevant hence reducing the potential employers’ processing time.
Brain Developmental Disorders’ Modelling based on Preschoolers Neuro-Physiological Profiling Abdul Wahab; Norhaslinda Kamaruddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp542-547

Abstract

Frequently misunderstood by their teachers as being low performers, children with learning disabilities (LDs) such as dyslexia, ADHD, and Asperger’s Syndrome develop low self-confidence and poor self-esteem that may lead to the risk of developing psychological and emotional problems. On contrary, research has shown that a substantial number of these children are capable of learning, and hence, are high-functioning. Therefore, there is a need to provide for the early detection of LDs and instruction that focuses on their needs based on their profiles. Profiling is normally done through observations on the psychological manifestations of LDs by parents and teachers as third-party observers. The first party experience, which is reflected through brain manifestations, is often overlooked. Hence the aim of this paper is to present an alternative solution to profile young children with LDs using electroencephalogram (EEG) that capture brain signals to measure brain functionalities and correlate them with the different LDs. Studies on neurophysiological signals and their relationship to LDs are used to develop Computational Neuro-Physiological (CN-P) model to be an alternative in quantifying the children brain activation function related to learning experience. It is envisaged that such model can profile children with learning disabilities to provide effective intervention in timely manner which can help teachers to provide differentiated instruction for children with LDs. This is in line with the thrust of the Education National Key Result Area (NKRA), the Malaysia Education Blueprint 2013-2025, and the Special Education Regulations 2013.
Assessment Analytic Theoretical Framework Based on Learners’ Continuous Learning Improvement M. Hamiz; M. Bakri; Norhaslinda Kamaruddin; Azlinah Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i2.pp682-687

Abstract

Currently, university students are required to follow stringent curriculum structure regardless of their performance. Personalized learning is not being offered resulting the whole cohort must compy to a customized fixed curriculum design. This is because the designed curriculum does not take into account different students’ attainment. Furthermore, there is a mismatched between supply and demand of graduates’ skill sets to fulfil the requirement of industry. Due to these issues, employers face difficulties in finding suitable high-skilled worker which contributes to large number of unemployed graduates. Thus, a systematic intervention of students’ learning process is essential to construct informed and strategic responses in order to manage challenges and minimize skill mismatch, at the same time providing adequate fundamental knowledge. In this paper, an assessment analytics framework is proposed based on automated extracted skill sets from curriculum documents and individual performance to recommend adaptive learners’ learning system (ALLS). By preparing the graduates with the required industry skill sets, the graduates’ unemployment rate is envisaged to reduce.
Jobseeker-industry matching system using automated keyword selection and visualization approach Norhaslinda Kamaruddin; Abdul Wahab Abdul Rahman; Ramizah Amirah Mohd Lawi
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i3.pp1124-1129

Abstract

Learning opportunities are available with the accessibility of new learning technologies, discovery of untraditional learning pathways and awareness of the importance of connecting current knowledge with new learning. Such situation allows the expansion in the number of courses, programs and professional certifications offered to the students resulting to the increment of the number of graduates annually. The graduates then employed by the industry for executing the job. However, there is a growing concern about the increment of unemployed graduates in the job market. One of the reasons of the mismatch between graduates’ skills and employers’ needs is that the jobseekers tend to choose wrong job because they are overwhelmed by the choices and typically they just randomly send the application because it is time consuming to filter relevant advert. Such action may have repercussion to the industry because the employers need to select relevant candidates to fill up the post from the unfiltered pile of applications making the selection process lengthy and time consuming. In this paper we proposed an automated approach to match the graduates’ and employers’ needs using a hybrid of text mining and visualization approach to facilitate jobseekers’ task of relevant job application. The important keywords are automatically extracted based on the frequency of the word used in the adverts. Then, the graduates’ skills are matched from their personalized profile. Relevant visualization approaches are incorporated to facilitate the selection. It is practical and feasible for the proposed approach to be incorporated in job searching websites that can optimize jobseekers and employers time and effort for a suitable match.
Interlaboratory data fusion repository system (InDFuRS) for tocotrienols-based treatment Norhaslinda Kamaruddin; Abdul Wahab
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i3.pp1130-1135

Abstract

Tocotrienols and tocopherols are part of the vitamin E family and have shown to produce lots of benefits especially in health supplement product. Both tocotrienols and tocopherols exist in an edible oil but varies in their ratio. It is also observed that percentage of tocopherols is higher than tocotrienols in most of our diet. Recent researches have found that tocotrienols seems to have more benefit to health especially for delaying neuro-degeneration and this has led researchers to investigate tocotrienols rich fraction (TRF) from palm kernel oil. To date, the tocotrienols extraction process is still work in progress. Hence, it is imperative that all information and results from the various laboratories experiments to be made available thus data analysis can be optimized for optimal tocotrinols production. Data acquisition from inter-laboratory experiments are valuable for collaborative researches. Efforts from multiple sources need to be combined to make it accessible for data integration. The sources of fused data can be employed as secondary back up once the data is migrated to a central repository. Traditionally data has been residing in silos across organization. Such scenario posed as a major problem especially when there are insufficient human and computational resources to manage such data. In addition, longitudinal data collections always suffer from mismanagement of the data where the data are not labeled properly using mismatched data formatting resulting to poor data readability. Therefore, a repository to facilitate data fusion using a systematic cloud-based system is proposed to ensure the data are accessible with maintained data uniformity and format and yet the security of the data is ensured as well as cost effective and fault tolerant. It is envisaged a better solution can be identified to minimize repetition of experiments and looking towards at advancement of extraction processes.
Co-Authors A. H. S. Nurhasmiza Abdul Wahab Abdul Wahab Abdul Rahman Abdul Wahab Abdul Rahman Abdul Wahab Abdul Rahman Azlinah Mohamed Dini Handiyani Frederick C. Harris Jr. Khairul Ikhwan Mohamad Halim M. Bakri M. Hamiz Mohd Hafiz Mohd Nasir Muhammad Hafiq Iqmal Mohd Noh N. A. S. Nilam Nur Azmina Mohamad Zamani Nur Shahana Saat Nurul Izzati Mat Razi P. Marlia Raja Durratun Safiyah Ramizah Amirah Mohd Lawi Srie Azrina Zulkiflie Yasmeen Rozaidi Z. Othman