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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science Startupreneur Business Digital (SABDA Journal)
Abdul Wahab Abdul Rahman
International Islamic University Malaysia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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Driver Behaviour State Recognition based on Speech Norhaslinda Kamaruddin; Abdul Wahab Abdul Rahman; Khairul Ikhwan Mohamad Halim; Muhammad Hafiq Iqmal Mohd Noh
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.8416

Abstract

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.
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.
The Influence of Entrepreneurial Mindset and Innovation on Startup Growth in the Digital Era Abdul Wahab Abdul Rahman; Tri Widiyatmoko; Mohamad Rakhmansyah
Startupreneur Business Digital (SABDA Journal) Vol. 4 No. 1 (2025): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sabda.v4i1.730

Abstract

This research aims to analyze the influence of entrepreneurial mindset and innovation on startup growth in the digital era. The background to this research is driven by the rapid development of technology which has changed the business landscape, so it is important for startups to have an innovative entrepreneurial mindset and be able to adapt quickly in the face of dynamic market changes. Startups that have a strong entrepreneurial mindset tend to be better able to identify opportunities, take measured risks, and survive in tight market competition. The object of this research is startups operating in the digital sector in Indonesia, with a focus on how entrepreneurial mindset and innovation influence their performance and growth. The method used is a quantitative approach, by distributing questionnaires to startup owners and managers to collect data regarding entrepreneurial mindset, innovation and growth. The collected data was analyzed using linear regression to test the relationship between these variables. The research results show that both entrepreneurial mindset and innovation have a significant positive influence on startup growth, with innovation acting as a mediator that strengthens this relationship. The conclusion of this research is that to achieve sustainable growth, startups need to develop an entrepreneurial mindset that supports creativity and innovation in every aspect of their operations. This research provides insight for startup players in optimizing their business development strategies in the digital era.
Co-Authors Khairul Ikhwan Mohamad Halim Mohamad Rakhmansyah Muhammad Hafiq Iqmal Mohd Noh Norhaslinda Kamaruddin Nurul Izzati Mat Razi Tri Widiyatmoko