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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences IAIC Transactions on Sustainable Digital Innovation (ITSDI) Startupreneur Business Digital (SABDA Journal) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi
Abdul Wahab Abdul Rahman
International Islamic University Malaysia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Other

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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)
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.
The Role of Artificial Intelligence in Strengthening Sharia-Based Educational Governance Rajendra Kumar; Abdul Wahab Abdul Rahman
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 5 No 1 (2026): September
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v5i1.1226

Abstract

The rapid development of Artificial Intelligence (AI) has created new opportunities for improving educational governance through more efficient decision-making, data management, transparency, and accountability across increasingly complex educational systems worldwide. However, its implementation in education requires alignment with ethical values and Islamic principles to ensure that technological advancement does not undermine fairness, privacy, responsibility, and human-centered decision-making. This study aims to examine the role of AI in strengthening Sharia-based educational governance and identify the opportunities, benefits, and challenges associated with its implementation. The study employs a Systematic Literature Review (SLR) by analyzing relevant academic publications that discuss AI applications, educational governance, Islamic ethics, and Sharia-based principles. The literature was reviewed systematically to identify recurring themes, relationships, and research gaps concerning the integration of AI into educational governance. The results indicate that AI can strengthen Sharia-based educational governance by supporting data-driven decision-making, improving administrative efficiency, enhancing transparency, and facilitating more accountable educational services. Nevertheless, the findings also reveal challenges related to data privacy, algorithmic bias, technological dependency, ethical responsibility, and the need for human oversight. In conclusion, AI has significant potential to support Sharia-based educational governance when its implementation is guided by Islamic ethical principles, transparency, accountability, justice, and responsible data management. This study contributes a conceptual understanding of how AI can be integrated into educational governance while maintaining the fundamental values of Sharia.
Transformer Architectures for Automated Brain Stroke Screening from MRI Images Abstract Husni Teja Sukmana; Zainal Arifin Hasibuan; Abdul Wahab Abdul Rahman; Luhur Bayuaji; Siti Ummi Masruroh
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.736

Abstract

Early and accurate detection of stroke is critical for timely medical intervention and improved patient outcomes. This study explores the application of deep learning models, particularly the Vision Transformer (ViT), for the automated classification of brain stroke from medical images. A curated dataset of brain scans was used to train and evaluate the ViT model, which was benchmarked against a widely used convolutional neural network (CNN), ResNet18. Both models were trained using transfer learning techniques under identical preprocessing and training configurations to ensure fair comparison. The results indicate that the ViT model significantly outperforms ResNet18 in terms of validation accuracy, class-wise precision, and recall, achieving a peak accuracy of 99.60%. Visual analyses, including confusion matrices and sample prediction comparisons, reveal that ViT is more robust in detecting subtle stroke patterns. However, ViT requires more computational resources, which may limit its deployment in real-time or low-resource settings. These findings suggest that transformer-based architectures are highly effective for medical image classification tasks, particularly in stroke diagnosis, and offer a viable alternative to traditional CNN-based approaches.
Market Expansion Through B2B Business Model Transformation into Vendor-Owned Digital Marketplaces Abdul Wahab Abdul Rahman; Noor Azura Binti Zakaria; Laurens Bethany
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 8 No 1 (2026): October
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v8i1.751

Abstract

Traditional business-to-business (B2B) vendor models rely on direct relationships, long-term contracts, and established distribution channels. These models provide stable transactions but limit market reach, increase dependence on a few buyers, and slow responses to changing demand. Prior studies explain why firms adopt digital platforms, but rarely how an established vendor redesigns its business model into its own marketplace, or how relationship-based channels can coexist with it. This study examines market expansion strategies through the transformation of traditional B2B vendor models into vendor-owned digital marketplaces, in which the vendor acts as platform owner rather than as a participant in a third-party marketplace. It uses a qualitative descriptive approach supported by a structured literature review and conceptual analysis. The review followed a structured procedure of identification, screening, eligibility assessment, and synthesis, and the selected studies were analyzed using thematic synthesis. The analysis shows that successful transformation requires more than adopting a digital platform. It depends on enabling capabilities, namely organizational readiness (including digital skills), digital infrastructure, process integration, customer data management, and digital marketing capability, and on channel governance that protects existing B2B relationships. The study proposes a staged hybrid framework of six constructs and six propositions, and identifies risks such as channel conflict, integration cost, and insufficient demand. Integrating traditional B2B strengths with digital marketplace capability creates a more flexible, scalable, and market-oriented business model. Market expansion emerges from capability-based transformation, not platform adoption alone. The propositions require empirical validation through survey-based or comparative case studies.
Co-Authors Husni Teja Sukmana Khairul Ikhwan Mohamad Halim Laurens Bethany Luhur Bayuaji Mohamad Rakhmansyah Muhammad Hafiq Iqmal Mohd Noh Noor Azura Binti Zakaria Norhaslinda Kamaruddin Nurul Izzati Mat Razi Rajendra Kumar Siti Ummi Masruroh Tri Widiyatmoko Zainal Arifin Hasibuan