JOURNAL INFORMATION AND TECHNOLOGY MANAGEMENT (JISTM)

DIGITAL ADVANCEMENTS IN RURAL PUBLIC LIBRARIES: OPPORTUNITIES AND BARRIERS TO SUSTAINABILITY

Mohd Hafidzrizal Mansor — 2025-03-10

This study explores user satisfaction with digital technology sustainability in rural public libraries, highlighting both opportunities and barriers to fostering long-term technological advancements in underserved communities. Leveraging data from 2,660 respondents, the research identifies critical challenges, including limited infrastructure, insufficient awareness, and gaps in service quality, which hinder effective integration and usage of digital technologies. Simultaneously, it underscores the potential of rural libraries to bridge digital divides by providing accessible, sustainability-oriented services that meet the evolving needs of users. Findings emphasize the need for targeted improvements in service quality, awareness campaigns, and infrastructure development to enhance user satisfaction and drive sustainable progress in rural digital initiatives.

IMPLEMENTATION OF INTEGRATED MANAGEMENT SYSTEM (IMS) FRAMEWORK FOR ISO 14001, 45001 & 55001 IN THE POWER GENERATION SECTOR

Manmit Singh Jasbeer Singh — 2025-03-30

The implementation of ISO MS in power generation sector are important to increase confidence level of stakeholders in the procedures and processes of the company. The power generation organization has three existing standalone ISO MS namely the ISO 14001, ISO 45001 & ISO 55001 and the organisation successfully implemented the Integrated Management System (IMS) where the common clauses and terms of the three separate MS are integrated into one “Integrated Manual”. The process of implementing IMS in the organisation begins with the development of a robust framework with the PDCA cycle followed by document review for level 1 and 2 documentations. The integration journey is supported by the quality secretariat as well as the top management to address teething issues. Working Committees at both station and HQ level support the implementation of the IMS across the organization while the top management provides support and guidance in the Steering Committee. The outcome of the IMS is a more efficient organization which has streamlined its ISO documentations and processes which has various benefits such as less duplication of paperwork, reduced management and certification cost as well as effective internal and external audits which support the Environment, Social and Governance (ESG) of the power generation organisation. This paper is beneficial as a reference for other power generation industries throughout the world who are keen to implement the IMS in their governance and quality management systems.

FRAUDULENT CREDIT CARD TRANSACTION DETECTION USING LOGISTIC REGRESSION

Syahir Aiman Shahrul Nadzman — 2025-03-20

Credit card fraud poses a significant threat to financial institutions and individuals, leading to substantial losses and undermining trust in digital payments. This study aimed to identify fraudulent transactions using a logistic regression-based machine learning model, develop a fraud detection prototype, and evaluate its accuracy using Precision-Recall Area Under the Curve (PR AUC). The methodology included three phases: Preliminary, Design, and Evaluation. In the Preliminary Phase, a literature review identified research gaps, and the September 2013 European credit card fraud dataset from Kaggle was preprocessed using robust scaling. The Design Phase involved constructing system architecture, creating flowcharts, designing a user interface, and developing logistic regression pseudocode. During the Evaluation Phase, the study balanced the dataset using undersampling, conducted 5-fold cross-validation, and split the data into training, testing, and validation sets in a 70:30 ratio. The logistic regression model was trained and evaluated using precision, recall, F1-score, and PR-AUC. The model achieved a PR-AUC score of 99.57% via the 10% validation set consisting of 52 fraud and 48 normal transactions, demonstrating high discriminatory power and reliability. The developed prototype enhances security and trust in digital payment systems. The use of robust scaling to normalise outliers, undersampling to balance the dataset, and comprehensive evaluation metrics provide valuable insights for future research and practical applications in fraud detection systems. This study contributes to mitigating credit card fraud and improving financial transaction integrity. Future work should encourage collaboration between financial institutions, regulatory bodies, and researchers to share various types of anonymised transaction data and best practices, which could lead to more robust and generalisable models.

CLOUD ADOPTION STRATEGIC PLANNING: LINEAR REGRESSION COEFFICIENT ANALYSIS

Azlinda Abdul Aziz — 2025-03-03

The implementation of eLearning in cloud environments significantly gives the benefit to Institutions of Higher Learning (IHLs). However, operating in a cloud environment presents a challenge for the IHLs. By adopting a cloud environment, the cloud adoption process relies on IHL decisions. Therefore, the cloud strategy must be coordinated with the strategic planning of IHL initially from the employees' response to using the eLearning cloud environment. Thus, this study is conducted to identify the factor adoption in using eLearning in a cloud environment. The document analysis from a literature review is conducted to find out the factors influencing adoption in using cloud environments. Moreover, the appropriate model as a basic model needed in the adoption of the cloud environment is defining. Thus, as a result of document analysis, a few factors adopted were classified, and the TOE model is selected, consisting of technology, organization, and environment context in document analysis. Furthermore, a preliminary study was conducted using interviews with employees who have experience with cloud environments at IHLs. The purpose is to integrate conceptual defining factors between the literature review and preliminary study to formulate the significant factor adoption for cloud environments. The factor adoption is classified as an independent factor in adopting a cloud network environment. The primary analysis showed that employee factors are needed and interact with motivation, benefits, and user awareness. Furthermore, the validation study was conducted, and the interaction between the defined factors was analyzed to demonstrate their impact using linear regression coefficient analysis. Thus, the outcome of the study shows a factor adoption contributed to the adoption of the eLearning cloud environment at IHL and gave the guidelines to IHL in implementing an eLearning in the cloud environment.

APPLICATION OF OPTIMIZATION TECHNIQUES IN THE RECYCLING SECTOR: A BIBLIOMETRIC ANALYSIS

Zurina Hanafi — 2025-03-24

Optimization techniques are vital for improving the operations of collection and recycling processes, reduction of waste and enhancement of recoverable resources. Recycling plays a significant role and optimization processes are essential for improving recycling efficiency. In this sense, these techniques assist in planning the best collection route, sorting method and resource distribution by applying mathematical models and algorithms. In recycling, optimization can help to choose the methods of processing materials, energy consumption and cost reduction. This study aims to identify the development, trends and research gaps related to the application of optimization techniques in the recycling sector. A bibliometric analysis was performed on 499 Scopus-indexed documents that have been published from 1978 to 2024. The key bibliometric methods used include citation analysis, co-authorship analysis, network analysis and performance analysis. The analysis shows that optimization in the recycling documents was generated from engineering and environmental science research. Journal of Cleaner Production is the source with the highest number of publications and citations. The countries contributing the most to this body of literature are China, followed by the United States. This paper contributes to an understanding of the research landscape, key influential works and collaboration networks in the application of optimization techniques in recycling.

AN EXPERIMENTAL INVESTIGATION ON DIFFERENT EPOCHS AND SPLITTING DATA RATIOS FOR STUDENT AUTHENTICATION SYSTEM USING CONVOLUTIONAL NEURAL NETWORK (CNN) BASED FACE RECOGNITION

Shaiful Bakhtiar Rodzman — 2025-03-20

In today's interconnected world, traditional username and password-based authentication methods are insufficient to safeguard sensitive data. This challenge is noticeable in Malaysian academic institutions, where these methods face vulnerabilities such as security breaches, forgotten passwords, and low user satisfaction. Weak passwords, reuse, and fake credentials further expose users to cyberattacks, highlighting the need for improved security and user experience. Face recognition using Convolutional Neural Networks (CNN) offers a promising solution, combining enhanced security with user-friendly identity verification. This study evaluates the performance of CNN based face recognition for improving authentication systems in Malaysian educational institutions. Experiments demonstrated the effectiveness of Student Authentication System Using CNN Based Face Recognition, achieving a maximum Average Recognition Accuracy of 100% and a minimum of 83% using varying epochs and data-splitting ratios. In conclusion, this approach has the potential to enhance security, usability, student experience, staff productivity, and institutional reputation.

A SECURITY ASSURANCE CASE FOR IoT SYSTEMS USING GOAL STRUCTURE NOTATION

Aftab Alam Janisar — 2025-03-30

IoT-focused cyberattacks had the largest attack surface, despite having a vast environment. Key security requirements (SR) for IoT include data confidentiality, data integrity, authentication, access control, privacy, etc. On the Internet of Things, confidentiality is a crucial security service and the most frequently targeted. Inadequate emphasis on assessment of IoT (SR) leads to attacks and threats. However, the absence of security requirement assessment in IoT systems architecture jeopardizes security, exposing the system to vulnerabilities, risking organizational assets and reputation, while also escalating the cost and time required to address security issues. An assurance case is developed for identification of security requirements assessment based on compliance standards. To communicate, align IoT security measures, and to identify, analyze, and address potential assets, security threats, and attacks systematically. In this research, a novel and illustrative example of assurance case is provided for the confidentiality security requirement of IoT system, to shed light on possible attacks and threats relevant to IoT assets. This process will help leverage a practical and clear basis for justifiable development of assurance case for IoT security requirement earlier and integration with RE activities. This structured approach will be vital across methodologies like Agile, Waterfall, and SSDL, ensuring compliance with security standards and offering a comprehensive solution to key challenges in IoT security.

ELIMINATING HIGH DENSITY SALT AND PEPPER NOISE FROM GRAYSCALE IMAGE USING ALPHA TRIMMED MEAN-MEDIAN FILTER

Mohd Saifunnaim Mat Zain — 2025-03-04

The use of images has increased over the previous decade, and they have the potential to be effective communication tools, similar to social media. In social media, uploading visual information or images seems to be becoming more popular. The appearance of noise disturbs the original information in the image. Thus, removing the noise before using the image for subsequent tasks is necessary. The approaches for image restoration are based on a mathematical model of image deterioration. Alpha trimmed mean median filter (ATMMF) is proposed as a new method for removing salt and pepper noise in digital images. The basic principle behind this method is that it starts with noise detection and then moves on to a filtering strategy. The experimental process was performed with 12 samples of grayscale images with a variable salt and pepper noise density ranging from 10% to 90% to compare the proposed method to other widely used methods. Afterward, PSNR and SSIM were taken as the quality measurements. The proposed filtering technique is simple to use and implement. Experimental results show that the proposed method has successfully reduced salt and pepper noise in high noise density. It outranks all the previous filtering methods regarding visual effects and quantitative measure results.

PREDICTING LIFE INSURANCE OWNERSHIP: THE ROLE OF SOCIOECONOMIC FACTORS

Ain'nul Mardhiah Dg. Md Nasir — 2025-03-20

Life insurance ownership is an important part of financial stability, yet ownership rates differ dramatically across income levels and demographic groupings. In order to encourage financial sustainability among a variety of demographics, it is crucial to comprehend the elements that influence life insurance ownership. This study utilizes logistic regression to predict the likelihood of life insurance ownership, with a particular focus on the impact of income and other socioeconomic factors. The findings show a substantial, positive association between income and the chance of carrying life insurance, with higher-income persons being much more likely to have policies. Education and marital status were also found to influence the probability of life insurance ownership. These results shed light on the socioeconomic variables influencing the purchase of life insurance by indicating that income differences are the main obstacle to life insurance accessibility. The study findings provide policymakers and insurers with recommendations for expanding life insurance coverage, especially among lower-income households. By identifying income disparities as a key barrier to accessibility, this research underscores the need for targeted strategies such as subsidized premium schemes, flexible payment options, and microinsurance products designed for affordability.

LEVERAGING MACHINE LEARNING FOR EARLY DETECTION OF MENTAL HEALTH ISSUES AMONG HIGHER EDUCATION STUDENTS

Muhammad Luqman Hakim Mohd Asni — 2025-03-13

Mental health disorders among higher education students have become a pressing global concern, with anxiety, depression, and stress significantly impacting academic performance, social relationships, and overall well-being. Early detection and intervention are critical to mitigating these challenges, yet traditional screening methods often fall short due to limited scalability, accessibility, and sensitivity to early signs of distress. This study explores the potential of machine learning (ML) to address these gaps by developing predictive models for identifying students at risk of mental health issues. The research utilized two datasets: a publicly available dataset from Kaggle and a custom dataset collected through an online survey administered to 212 university students. The survey captured diverse dimensions, including demographic, academic, psychological, and social factors, ensuring a comprehensive understanding of the variables influencing mental health. Multiple ML algorithms, including Decision Trees, Support Vector Machines, and Neural Networks, were applied to analyze the data and identify key predictors of mental health risks. The resulting predictive model demonstrated commendable accuracy, highlighting its potential utility for early intervention in educational settings.

THE FUTURE OF MOBILE BANKING ADOPTION: NEW INSIGHTS FROM CONSUMER BEHAVIOR AND THE TECHNOLOGY ACCEPTANCE MODEL

Zaidie Chen — 2025-03-30

This study investigates the influence of perceived external control and computer playfulness on behavioral intention to adopt mobile banking, with perceived ease of use (PEOU) as a mediator. Mobile banking adoption is driven by factors beyond basic usability, including security, personalization, and trust. While the Technology Acceptance Model (TAM) provides a foundation for understanding user acceptance, this research extends it by focusing on the roles of perceived external control (beliefs about external factors impacting banking outcomes) and computer playfulness (enjoyment derived from the user experience). Previous studies suggest that financial control significantly affects use intentions, with ease of navigation enhancing users' sense of control. Similarly, the enjoyment derived from mobile banking increases engagement and adoption. However, security and privacy concerns remain paramount. The Technology Acceptance Model's third version, TAM3, which incorporates social influence and external factors, acknowledges the complexity of technology adoption, but its focus on individual-level influences and complexity can be seen as limitations. Therefore, this study explores the importance of integrating additional constructs, such as trust and emotional responses, along with regulatory compliance, to develop a more comprehensive understanding of user behavior. This study examines the interplay of computer playfulness and perceived external control with mobile banking adoption, seeking to determine which factors have the most significant impact on user behavior. These insights are intended to help financial institutions optimize their mobile banking offerings through user-centric design and robust security measures to promote widespread adoption.

MACHINE LEARNING FOR SUSTAINABLE AGRICULTURE: ENHANCING PADDY LEAF DISEASE DETECTION USING CONVOLUTIONAL NEURAL NETWORK

Salehuddin Shuib — 2025-03-20

Sustainable agriculture, crucial for long-term food security, faces challenges in maintaining rice yields amidst growing global demands, particularly in regions like Malaysia where rice is a staple. Early and accurate detection of paddy diseases is vital to minimize crop losses, but traditional manual inspection methods are time-consuming and often inaccurate. This paper addresses the need for a more efficient solution by using Convolutional Neural Network (CNN) model based on the InceptionV3 architecture to detect four common paddy leaf diseases: Bacterial Leaf Blight, Brown Spot, Leaf Smut and Hispa. The model was trained using datasets from Kaggle, employing data preprocessing and augmentation techniques to enhance accuracy. The results show high accuracy (95%), in classifying the targeted diseases, demonstrating its potential for real-world deployment in automated disease detection systems. This study presents a viable solution for modernizing paddy disease detection and management, offering a scalable tool for sustainable agriculture practices that can reduce crop losses and bolster food security amidst growing global demands.

ENHANCING ZAKAT MANAGEMENT THROUGH DIGITALIZATION: A LITERATURE REVIEW ON EMERGING TECHNOLOGIES AND BEST PRACTICES

Farahdina Fazial — 2025-03-10

The digitalization of zakat management has emerged as a critical solution to address longstanding challenges such as inefficiency, lack of transparency, and limited accessibility in traditional zakat systems. Despite the growing adoption of digital technologies like blockchain, artificial intelligence (AI), and digital platforms, significant gaps remain in understanding their impact on zakat operations and the barriers to their implementation. This study aims to review the role of emerging technologies in enhancing zakat management,, review key challenges faced during digitalization, and analyse actionable recommendations for practitioners and policymakers.Employing a systematic and data-driven methodology, this study utilized Scopus AI to conduct a comprehensive review of peer-reviewed literature published between 2018 and 2025. Findings indicate that digital technologies have increased the effectiveness and transparency of zakat collection and distribution to a significant extent, enabling real-time tracking, reducing administrative costs, and gaining stakeholders' trust. Blockchain enables secure and impenetrable records, while AI improves decision-making capabilities using predictive analysis and automation. However, issues such as data security concerns, limited technological infrastructure, and resistance hinder its extensive uptake. The study further stresses the requirement for capacity development programs, regulatory frameworks, and collaboration between zakat institutions and technology firms in overcoming these problems. Theoretical contributions enrich literature on digitalization in Islamic finance, whereas applied recommendations offer experience into initiating scaleable and sustainable digital zakat platforms. Whereas the study provides useful findings, secondary data utilisation and analysis on specific locations limit its global applicability. Follow-up studies should explore empirical evidence from underserved areas and examine the long-term impacts of developing technology on zakat management. This study emphasizes the potential of digitalization to revolutionize zakat systems, making it easier to distribute resources in a balanced manner and overall welfare.

ENHANCING KNOWLEDGE SECURITY THROUGH TEXT STEGANOGRAPHY: A REVIEW OF TECHNIQUES AND TOOLS

Mohd Hilal Muhammad — 2025-03-30

This study addresses the increasing challenges of ensuring data security and privacy in the digital age, specifically focusing on the limitations of current steganography techniques in maintaining a balance between embedding capacity and the naturalness of text. As digital threats continue to evolve, traditional steganography methods, though effective in concealing information, often compromise the coherence of the text or limit the volume of hidden data. The aim of this study is to explore and analyze emerging technologies and methods that enhance the robustness of text steganography while preserving the integrity and readability of the text. The literature review highlights various approaches, such as synonym substitution, sentence restructuring, and the use of invisible characters, as well as advanced techniques like deep learning and generative adversarial networks (GANs) to improve data embedding. It also examines tools like Steghide, OpenStego, and SilentEye, which are widely used for steganography, yet face challenges in security and usability. The main findings indicate that AI and machine learning-driven techniques significantly improve the capacity and imperceptibility of hidden data while minimizing detection through steganalysis. Additionally, integrating steganography with cryptography and blockchain technologies offers enhanced security by ensuring that hidden data remains protected even if detected. These findings have significant implications for secure communication, suggesting that further refinement of these methods could lead to more robust and adaptive steganographic systems capable of withstanding increasingly sophisticated detection techniques. This study encourages ongoing research into the development of new tools and technologies to address the growing need for secure and covert data transmission.This abstract provides a concise summary of the study, highlighting the problem, aims, literature review, findings, and implications.

BRIDGING THE GAPS: EVALUATING CYBERSECURITY AWARENESS AND PRACTICES FOR ENHANCED DIGITAL SECURITY

Andria — 2025-03-20

Cybersecurity threats become a normal phenomenal in our daily life due to the advancement of Internet technology and the widespread use of it. To mitigate the risk aside from the advantages of these technology, one of the ways is to educate the users. In this preliminary study we evaluate cybersecurity awareness, practices, and preparedness among respondents, focusing on their knowledge of fundamental concepts, adherence to institutional policies, and incident response capabilities. There are 35 respondents who are among the schoolteachers who taught basic computer subject in various secondary schools in Kuala Muda and Yan districts of Kedah. While 77.7% of respondents are familiar with cybersecurity concepts and 100% demonstrate awareness of critical issues such as phishing and strong passwords, gaps persist in applying cybersecurity measures. Inconsistent password hygiene, low training participation (84.9%), and limited confidence in incident response are notable challenges. Furthermore, only 27.3% of respondents are well-versed in institutional cybersecurity policies. These findings emphasize the need for improved training programs, clearer policy communication, and enhanced institutional support to bolster readiness. Recommendations include expanding educational initiatives, mandating regular password updates, and refining incident response protocols. Addressing these gaps can strengthen organizational cybersecurity defences and create a more secure digital environment, highlighting the urgency of proactive measures to bridge the gap between knowledge and practical implementation.

GOOD GOVERNANCE PRACTICES IN MALAYSIA: THE ROLES OF DIGITAL TRANSFORMATION

Shahril Ahmad — 2025-03-04

Good governance is essential for Malaysia's prosperity. For Malaysia to be successful in achieving its objectives, it requires an intricate strategy. Malaysia should adopt digitalization initiatives like developed nations to improve governance. For the purpose of enhancing productivity, economic growth, and the quality of government services, Malaysia has aggressively implemented digitalization across a variety of sectors. However, even when compared to the results of other developing countries, the results of the good governance index are unsatisfactory. During the past ten years, there has been some progress made, but it is not sufficient. With the intention of bolstering the digital economy, Malaysia launched MyDigital 2021-2030. In order to achieve national prosperity, initiatives pertaining to digital transformation ought to incorporate all of the principles of good governance. When incorporating each principle into digitalization initiatives, it is necessary to ensure that Systems Theory is taken into consideration. In order to achieve good governance, digital technologies that are based on systems theory present challenges. Concerns of significant importance include the digital divide, cybersecurity, literacy, interoperability, and all aspects of data integration. Collaboration between government agencies, civil society organisations, and the private sector is required in order to digitalize good governance in Malaysia. This is necessary in order to address issues and challenges. The evaluation of the outcomes of digital governance projects also requires the implementation of systems that provide continuous monitoring, assessment, and feedback in order to guarantee the projects' long-term viability and operational efficiency. It is possible that Malaysia will fall behind other developing countries if it does not exploit the opportunities presented by the digital transformation.

MAPPING SMES’ INTENTION TO ADOPT INFORMATION SYSTEM TECHNOLOGY: THEORIES, DETERMINANTS AND INTEGRATED FRAMEWORKS

Elma Dewiyana Ismail — 2025-03-30

Information System (IS) technology can enhance operational efficiency, strategic decision making, and competitiveness among Small and Medium Enterprises (SME). However, studies on the theoretical landscape and key determinants of SMEs’ intention to adopt remain fragmented and under-researched. The objectives of this study is to examine the core theoretical frameworks commonly used and to identify key determinants across different contexts and technology. This narrative review uses a qualitative and quantitative approach where it leverages Scopus and Google Scholar databases to retrieve relevant articles to be analyzed. Grounded on studies exploring information system technology, such as artificial intelligence (AI), blockchain technology, building information modelling (BIM), and digitalization, organization of theoretical models including the Technology Organization Environment (TOE) framework, Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and other theories are presented. Interesting findings discovered that, in addition to common determinants, such as technology considerations, organizational context, environmental factors, and psychological aspects, play a significant role in the decision-making process to adopt a technology. Through the synthesis process, this narrative review stresses the multi-theoretical attempts, which allows for better understanding and guidance for SMEs on their intention to adopt information system technology.

INTELLIGENT MODELS FOR INTRUSION DETECTION OVER CLOUD INFRASTRUCTURE: A LITERATURE REVIEW

Mansir Abubakar — 2025-03-20

Cloud Computing has revolutionized the information technology (IT) landscape, enabling scalable and on-demand access to resources. However, its reliance on shared infrastructure introduces vulnerabilities, necessitating advanced security measures. Traditional intrusion detection systems (IDSs) struggle to cope with the complexity and scale of cloud environments. Machine Learning (ML) has emerged as a promising approach, offering automation, adaptability, and enhanced detection capabilities, thus, ensuring intelligence in intrusion detection systems. With the increasing reliance on cloud infrastructure for critical applications, ensuring robust security measures has become paramount. This paper reviews existing works that employ Machine Learning (ML) techniques for intrusion detection in cloud environments. By analyzing the strengths and weaknesses of these models, we identify gaps in current approaches and propose potential research directions. Furthermore, we recommend advanced ML techniques to enhance the security and reliability of cloud-based systems. The existing literature revealed that the transition from conventional methods to advanced learning approaches signals a critical shift in the landscape of cloud-based security, although the literature disclosed that further research is necessary to refine these models and enhance their effectiveness across diverse attack vectors.

RASCH VALIDATION OF THE WBISA APPLICATION QUESTIONNAIRE IN HIGHER LEARNING INSTITUTION

Nur Razia Mohd Suradi — 2025-03-03

It is important to access the quality of any academic application in higher learning institution. Basically, users of this application are academicians who rely on system to perform daily activities. However, limited study has been conducted to investigate this matter. Constructing an item questionnaire with a content validity, reliability and fairness is not easy. The questionnaire was then distributed among the academicians who used WBISA application via online platform. Therefore, this study aims to reveal to develop the item of the questionnaire using Rasch measurement model. The questionnaire consists of 61 items used with 9 categories of characteristics. Overall, the results have shown that item fits were within an acceptable range. Meanwhile, the Cronbach’s alpha was a good value at 0.97, with person reliability of 0.96 and item reliability of 0.87. The person separation was observed at a value of 5.05 while the item separation is considered acceptable at a value of 2.54; indicating good measurement capability which supports its use in evaluating academician's using WBISA application. Finally, a well-developed of item questionnaires has been established.

GENDER DIFFERENCES IN THE EFFECTIVENESS OF GAMIFICATION IN MATHEMATICS EDUCATION AMONG SECONDARY SCHOOL STUDENTS IN MUKAH, SARAWAK

Puteri Faida Alya Zainuddin — 2025-03-20

Even though gamification has gained popularity in education, limited research has been conducted on gender differences in how gamified approaches are implemented in secondary school mathematics. This study examined the effect of gamification on 59 students from three different secondary schools in Mukah, Sarawak, who participated in a one-day mathematics program. This study found that female students are more likely to be motivated, experience greater enjoyment, have greater perceptions of usefulness and intend to use the mathematics gamification than male students. Statistically significant differences are found in this regard. Besides, the data showed that both male and female students' perceived usefulness was significantly related to their enjoyment and willingness to use mathematics gamification but not their motivation and engagement. It is clear from these results that educators must consider gender-specific responses in order to make an effective mathematics gamification. However, the results of this study can only be applied to the program since nonparametric analyses were used. Thus, larger studies with broader participation and pre-post evaluations may be useful for future research to obtain more robust evidence concerning the differences between the genders.

VANET TRAFFIC SIMULATION FOR BLACKHOLE ATTACK DETECTION USING AODV ROUTING PROTOCOL

Ahmad Yusri Dak — 2025-03-20

Vehicular Ad-hoc Networks (VANETs) play a pivotal role in modern intelligent transportation systems by enabling seamless Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. However, their dynamic and decentralized nature exposes them to security vulnerabilities, particularly Blackhole attacks, where malicious nodes disrupt network operations by advertising false routes and dropping packets. This study evaluates the impact of Blackhole attacks on VANET performance using the Ad-hoc On-Demand Distance Vector (AODV) routing protocol. Simulations were conducted in NS-2 with BonnMotion mobility models, varying node densities (20–60 nodes) within a 1000×1000 m² area over 140 seconds to emulate urban traffic congestion. Key metrics such as End-to-End Delay (EED), Packet Delivery Ratio (PDR), Throughput, Goodput, and Packet Loss Rate (PLR) were analysed under normal and attack scenarios. Results revealed severe network degradation during attacks: EED surged by 63.43% (from baseline 175.05 ms), PLR exceeded 80% consistently, and PDR plummeted drastically (e.g., from 99.78% to 10.01% for 60 nodes). Throughput declined by up to 85% (e.g., 46.94 Kbps to 6.84 Kbps for 60 nodes), while Goodput exhibited similar deterioration due to malicious packet drops. Notably, higher node density exacerbated congestion and attack impacts, underscoring the vulnerability of scalable VANETs. The findings highlight the Blackhole attack’s crippling effects on data reliability and real-time communication, critical for safety applications like emergency messaging and traffic management. This study underscores the urgent need for robust mitigation strategies, including trust-based protocols, intrusion detection systems, and adaptive routing algorithms, to safeguard VANETs against such threats. By addressing these vulnerabilities, this research advances secure, efficient vehicular communication frameworks, ensuring the operational integrity and safety of future intelligent transportation ecosystems.

INVESTIGATION OF INDOOR WI-FI STABILITY BASED ON THE RECEIVED SIGNAL STRENGTH INDICATOR (RSSI): A CASE STUDY AT UC TATI HOSTEL

Mohd Shah — 2025-03-30

Wi-Fi has become indispensable in today’s digital era, enabling connectivity in homes, workplaces, and educational institutions. However, ensuring stable indoor Wi-Fi remains a challenge due to factors such as structural obstacles, interference, and varying user loads, all of which impact signal strength. This study examines indoor Wi-Fi stability at UC TATI Hostel, aiming to achieve an optimal RSSI value of -67 dBm using Ekahau AI Pro and the WiFiman app for analysis. Various scenarios were assessed, including different AP types, placements, and user loads. Findings revealed that wall-mounted APs outperformed ceiling-mounted ones, and modern APs with advanced features were more effective than older models, particularly under heavy user loads. Additionally, environmental factors like interference from lighting influenced performance. The study offers practical recommendations for enhancing Wi-Fi in similar indoor settings, emphasizing the importance of upgrading to modern APs, strategic AP placement, and reducing interference.

EXPLORING E-BOOKS EVALUATION: ISSUES, EVALUATION CRITERIA AND TECHNIQUES

Noor Azura Zakaria — 2025-03-20

E-book evaluation involves balancing technical and user-centered criteria to ensure usability and functionality across various contexts and platforms. This study reviews existing literature to examine the issues, challenges, evaluation criteria, and techniques used in e-book evaluation. Identified challenges include the lack of standardization in interfaces and functionalities, licensing restrictions such as Digital Rights Management (DRM), variability in accessibility features across formats, and the complexity of criteria influenced by context and user needs. Commonly used evaluation criteria include readability, accessibility, interactivity, efficiency, and navigation. The evaluation approaches vary across domains, with educational settings focusing on engagement and knowledge retention, academic contexts prioritizing accuracy and depth, and professional environments emphasizing practicality and efficiency. Methodologies such as mixed methods, usability testing, and surveys are used to measure user satisfaction and technical performance. This review offers valuable insights for developers, educators, and researchers. Additionally, guiding better user experiences in future e-book design and innovation.

INTEGRATING BLOCKCHAIN, AI, AND RFID TECHNOLOGIES TO COMBAT COUNTERFEITING IN SUPPLY CHAIN MANAGEMENT: A COMPREHENSIVE LITERATURE REVIEW

Muhammad Khairul Zharif Nor A’zam — 2025-03-13

Counterfeiting in supply chain management poses significant challenges, including economic losses, compromised product safety, and damage to brand reputation. The increasing complexity and globalization of supply chains have exacerbated these issues, making it difficult for organizations to ensure the authenticity and traceability of products. The purpose of this research is to investigate how blockchain, AI, and RFID technologies can be used to provide complete protection against supply chain counterfeiting. Blockchain guarantees tamper-proof transaction records by ensuring decentralization, immutability, and transparency. Real-time tracking and physical-layer identification made possible by RFID help to lower the risk of fake products finding their way onto the supply chain. Through pattern analysis, automated verification, and anomaly identification suggesting fraudulent activity, artificial intelligence improves detection capacity. The findings reveal that the integration of these technologies creates a robust framework for ensuring end-to-end traceability, enhancing security, and improving operational efficiency. For instance, IoT-based secure medicine supply chains and electronic component authentication systems demonstrate the practical effectiveness of this approach. Despite its promise, challenges such as technical complexity, scalability, and adoption barriers remain. These include the need for secure communication protocols between RFID tags and blockchain nodes, as well as the cost and expertise required for implementation. This study has important ramifications since the suggested structure not only solves the immediate problem of counterfeiting but also creates the basis for more open, effective, and strong supply networks. Future research should focus on overcoming these challenges by developing scalable solutions, cost-effective implementations, and standardized regulatory frameworks. Organizations that use Blockchain, AI, and RFID can dramatically reduce the risk of counterfeit items, ensuring the legitimacy and safety of commodities from manufacture to distribution.

BUILDING RESILIENT FOOD SYSTEMS: INTEGRATING ONTOLOGIES IN SEJAHTERA-SOUQ FOR IIUM'S SUSTAINABLE MARKETPLACE

Mohd Khairul Azmi Hassan — 2025-03-30

Sustainable food systems are integral to addressing global challenges such as food security, environmental degradation, and socio-economic resilience. As the demand for sustainable practices grows, ontologies provide a powerful means to organize, manage, and analyze the complexity of these systems. This paper examines the integration of ontologies into the Sejahtera-Souq, a marketplace initiative at the International Islamic University Malaysia (IIUM), to bolster food system resilience. Ontologies allow for a structured representation of data across the food supply chain, enabling automated sustainability assessments, greater transparency, and more efficient operations. Sejahtera-Souq, in line with IIUM’s sustainability goals, stands to benefit significantly from ontology-based tools. By adopting such frameworks, the marketplace can track and manage food and optimize resource use. Moreover, ontologies can aid in mapping relationships between food products, their origins, processing methods, and their environmental impacts. This will facilitate informed decision-making for both producers and consumers, promoting a culture of sustainability on campus. This study not only reviews current applications of ontologies in food systems but also proposes a roadmap for integrating these technologies into Sejahtera-Souq. Through the use of ontology-driven intelligence, the marketplace can align itself with international standards for sustainability while fostering local socio-economic growth. The incorporation of these technologies into Sejahtera-Souq can serve as a model for other institutions seeking to implement sustainable marketplace frameworks, demonstrating how academic institutions can play a leading role in the global shift towards more resilient food systems.

REVOLUTIONIZING AUTISM CARE: THE ROLE OF AI-DRIVEN TOOLS IN ENHANCING INTERVENTIONS FOR CHILDREN WITH AUTISM SPECTRUM DISORDER

Jasmin Ilyani Ahmad — 2025-03-20

Autism Spectrum Disorder (ASD) is a condition related to brain development that affects social interaction and communication. Among the symptoms of ASD are reduced eye contact, differences in body language, lack of facial expressions and repeating gestures or sounds. There are various traditional treatment methods such as communication therapy, but there are also many challenges that need to be faced such as lack of resources, high costs, limited access, and a lack of tailored solutions. With the existence of Artificial intelligence (AI), various techniques, applications and devices are introduced to overcome this problem by using tools such as machine learning, virtual reality, and AI-driven wearables. This technology has great potential for early diagnosis, personalized therapy, communication support, and real-time progress tracking. It also helps in reducing costs, fill resource gaps, and give caregivers practical insights. Although there are many advantages of AI, challenges such as data quality, algorithm bias, privacy concerns, and accessibility still need to be seen. Experts emphasize the need for collaboration across disciplines and ethical practices to ensure these tools are effective and fair. With continued research and thoughtful application, AI could also support other developmental conditions, making healthcare more personalized and inclusive for children with ASD and their families.