Articles
<![CDATA[Driver Behaviour State Recognition based on Speech ]]>
<![CDATA[Norhaslinda Kamaruddin]]>;
<![CDATA[Abdul Wahab Abdul Rahman]]>;
<![CDATA[Khairul Ikhwan Mohamad Halim]]>;
<![CDATA[Muhammad Hafiq Iqmal Mohd Noh]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v16i2.8416
<![CDATA[Researches have linked the cause of traffic accident to driver behavior and some studies provided practical preventive measures based on different input sources. Due to its simplicity to collect, speech can be used as one of the input. The emotion information gathered from speech can be used to measure driver behavior state based on the hypothesis that emotion influences driver behavior. However, the massive amount of driving speech data may hinder optimal performance of processing and analyzing the data due to the computational complexity and time constraint. This paper presents a silence removal approach using Short Term Energy (STE) and Zero Crossing Rate (ZCR) in the pre-processing phase to reduce the unnecessary processing. Mel Frequency Cepstral Coefficient (MFCC) feature extraction method coupled with Multi-Layer Perceptron (MLP) classifier are employed to get the driver behavior state recognition performance. Experimental results demonstrated that the proposed approach can obtain comparable performance with accuracy ranging between 58.7% and 76.6% to differentiate four driver behavior states, namely; talking through mobile phone, laughing, sleepy and normal driving. It is envisaged that such approach can be extended for a more comprehensive driver behavior identification system that may acts as an embedded warning system for sleepy driver.]]>
<![CDATA[Correlation of learning disabilities to porn addiction based on EEG ]]>
<![CDATA[Norhaslinda Kamaruddin]]>;
<![CDATA[Nurul Izzati Mat Razi]]>;
<![CDATA[Abdul Wahab]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v10i1.2462
<![CDATA[Researchers were able to correlate porn addiction based on electroencephalogram (EEG) signal analysis to the psychological instruments’ findings. In this paper we attempt to correlate the porn addiction to various cases of learning disorders through analyzing EEG signals. Since porn addiction involved the brainwave power at the frontal of the brain, which reflects the executive functions, this may have correlation to learning disorder. Only three types of learning disorder will be of interest in our study involving dyslexic, attention deficit and hyperactivity disorder (ADHD) and autistic children because they involved reduced intellectual ability observed from the lack of listening, speaking, reading, writing, reasoning, or mathematical proficiencies. Children with such disorder when expose to the internet unfiltered porn contents may have minimal understanding of the negative effects of the contents. Such unmonitored exposure to pornographic contents may result to porn addiction because it may trigger excitement and induced pleasure. Experimental results show strong correlation of learning disorders to porn addiction, which can be worthwhile for further analysis. In addition, this paper also indicates that analyzing brainwave patterns could provide a better insight into predicting and detecting children with learning disorders and addiction with direct analysis of the brain wave patterns.]]>
<![CDATA[Small and medium enterprise business solutions using data visualization ]]>
<![CDATA[Norhaslinda Kamaruddin]]>;
<![CDATA[Raja Durratun Safiyah]]>;
<![CDATA[Abdul Wahab]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v9i6.2463
<![CDATA[The small and medium enterprise (SME) companies optimize performance using different automated systems to highlight the operations concerns. However, lack of efficient visualization in reporting results in slow feedbacks, difficulties in extracting root cause, and minimal corrective actions. To complicate matters, the data heterogeneity has intensely increased, and it is produced in a fast manner making it unmanageable if the traditional methods of analytics are applied. Hence, we propose the use of a dashboard that can summarize the operational events using real-time data based on the data visualization approach. This proposed solution summarizes the raw data, which allows the user to make informed decisions that can give a positive impact on business performance. An interactive intelligent dashboard for SME (iid-SME) is developed to tackle issues such as measurement of cases completed, the duration of time needed to solve a case, the individual performance of handling cases and other tasks as a proof of concept. From the result, the implementation of the iid-SME approach simplifies the conveyance of the message and helps the SME personnel to make decisions. With the positive feedback obtained, it is envisaged that such a solution can be further employed for SME improvement for better profit and decision making. ]]>
<![CDATA[Effective tocotrienol dosage traceability system using blockchain technology ]]>
<![CDATA[Norhaslinda Kamaruddin]]>;
<![CDATA[Abdul Wahab]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v9i4.2067
<![CDATA[Tocotrienol dosage, especially in vitamin E, is important for treatment and prevention of diseases. To date, the dosage is given based on the physician's knowledge and experience to suit the patient’s needs. The alteration of the dosage is depending on the way the patient’s body reaction and coping mechanism which is different from one to another. Hence, the optimal dosage is very difficult to achieve and may result in undesirable side effects. An alternative solution using blockchain technology to trace and chart the dosage of tocotrienol is proposed to capture the effective measure for the patient. With the advancement of the internet of things (IoT) and big data analytics technologies, an effective tocotrienol dosage is possible by utilizing the data gathered from the individual patient for tocotrienol dosage personalization profiling. Then, the output can be used to assist the physician to diagnose an appropriate amount of tocotrienol dosage for optimum effect. This paper discusses the theoretical framework of using blockchain technology to develop an effective tocotrienol dosage traceability system. It is envisaged that such an approach can be a guide to the health practitioners to administer the correct dosage for the patient and subsequently leads to a better quality of life.]]>
<![CDATA[Dynamic navigation indoor map using Wi-Fi fingerprinting mobile technology ]]>
<![CDATA[Srie Azrina Zulkiflie]]>;
<![CDATA[Norhaslinda Kamaruddin]]>;
<![CDATA[Abdul Wahab]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v9i2.2066
<![CDATA[This paper presents the exploitation of Wi-Fi signals sensors using fingerprinting method to capture the location and provide the possible navigation paths. Such approach is practical because current smartphones nowadays are equipped with inertial sensors that can capture the Wi-Fi signals from the Wi-Fi’s access points inside the building. From the comparative study conducted, the AnyPlace development tool is used for the development of dynamic navigation indoor map. Its components, namely; Architect, Viewer, Navigator and Logger are used for different specific functions. As a case study, we implement the proposed approach to guide user for navigation in Sunway Pyramid Shopping Mall, Malaysia as floor plan as well as using Google Maps as the base map for prove of concept. From the developer point of view, it is observed that the proposed approach is viable to create a dynamic navigation indoor map provided that the floor plans must be generated first. Such plan should be integrated with the SDK tool to work with the navigation APIs. It is hoped that the proposed work can be extended for more complex indoor map for better implementation.]]>
<![CDATA[Pornography Addiction Detection based on Neurophysiological Computational Approach ]]>
<![CDATA[Norhaslinda Kamaruddin]]>;
<![CDATA[Abdul Wahab Abdul Rahman]]>;
<![CDATA[Dini Handiyani]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 1: April 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v10.i1.pp138-145
<![CDATA[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.]]>
<![CDATA[Detecting learning disabilities based on neuro-physiological interface of affect (NPIoA) ]]>
<![CDATA[Nurul Izzati Mat Razi]]>;
<![CDATA[Abdul Wahab Abdul Rahman]]>;
<![CDATA[Norhaslinda Kamaruddin]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 1: July 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v19.i1.pp164-171
<![CDATA[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.]]>
<![CDATA[Neuro-physiological porn addiction detection using machine learning approach ]]>
<![CDATA[Norhaslinda Kamaruddin]]>;
<![CDATA[Abdul Wahab]]>;
<![CDATA[Yasmeen Rozaidi]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v16.i2.pp964-971
<![CDATA[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.]]>
<![CDATA[Insights extraction on cross-cultural interaction through astronomy online labs using data analytics ]]>
<![CDATA[M. Bakri]]>;
<![CDATA[Norhaslinda Kamaruddin]]>;
<![CDATA[M. Hamiz]]>;
<![CDATA[P. Marlia]]>;
<![CDATA[A. H. S. Nurhasmiza]]>;
<![CDATA[Z. Othman]]>;
<![CDATA[N. A. S. Nilam]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v16.i1.pp508-515
<![CDATA[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.]]>
<![CDATA[Visualization of job availability based on text analytics localization approach ]]>
<![CDATA[Nur Azmina Mohamad Zamani]]>;
<![CDATA[Norhaslinda Kamaruddin]]>;
<![CDATA[Abdul Wahab]]>;
<![CDATA[Nur Shahana Saat]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v16.i2.pp744-751
<![CDATA[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.]]>
