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Exploration of The Impact of Cyber Situational Awareness On Small and Medium Enterprises (SMEs) in Malaysia
No full textThe objective of this study is to explore the cyber situational awareness (CSA) level among the employees of small and medium-sized enterprises (SME) in Malaysia, by extending Endsley's situation awareness (SA) theory. It is crucial to understand the level of cyber situational awareness among employees as it sheds light on how well the employees understand the cyber threats and if they can handle them effectively. Literature has reviewed that SMEs are subject to a greater danger of cyber-attacks. Therefore, employees' awareness of cyber situations is of the utmost significance in studying cyber security. A convenient non-probability sampling method was chosen due to less expensive to deploy and increase the efficiency of data collection processes. IBM SPSS was used to conduct descriptive exploration data analysis that provides insight into the employee's current CSA by categorizing the employees into good, average, and poor understanding of the CSA. A total of 443 surveys were collected in the study, the findings reveal that most employees are not adequately aware of cyber situations, and employees understand the need to adhere to cyber security policy within the organization but fail to comply. The study contributes to practical domain by identifying the current level of CSA, SMEs should be set forth to create a strong culture of cyber security awareness and compliance and prioritize cyber security as part of the organization's culture to improve overall employee engagement and motivation in dealing with cyber threats
Climate Change Analysis in Malaysia Using Machine Learning
No full textClimate change presents significant challenges to ecosystems, economies, and societies globally. In Malaysia, a tropical country highly dependent on its natural resources, the impacts are evident in altered rainfall patterns, rising temperatures, and extreme weather events. Despite these challenges, many studies still predominantly rely on traditional statistical methods, which limit their capacity for making accurate climate predictions and developing effective policy solutions.This study effectively addresses the existing gap in research by analyzing extensive historical climate data using advanced machine learning (ML) techniques. The primary focus is on accurately forecasting trends in both precipitation patterns and surface air temperature fluctuations. Performance measures like Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are used to assess three ML models: Support Vector Regression (SVR), Random Forest Regression (RFR) and Linear Regression (LR). The findings demonstrate that LR performs better than the other models in forecasting patterns of precipitation and temperature. The results suggest a significant increase in temperature and unpredictable patterns of precipitation, and that poses major implications for agriculture, infrastructure resilience, and water management. Malaysia's climate resilience is improved by this research, which promotes data-driven policymaking by assessing current climate adaptation methods and offering practical ideas
Image-based Detection and Classification of Poultry Diseases from Chicken Droppings in Open House Poultry Farms
No full textMonitoring chicken health is essential for maintaining the production efficiency of poultry farms and meeting the demand for poultry products. Previous studies have explored various methods, including utilizing sound, behaviour, and the shape of the chickens, as well as the conditions of their droppings, to assess chicken health. In this research, we monitor chicken droppings as a reliable indicator of chicken health. We develop an automated system for detecting chicken droppings and identifying health conditions, specifically in open house poultry farms in Malaysia. Open poultry houses are the most common design in Malaysia due to their lower construction and maintenance costs, a more natural environment for the chickens, and greater space to roam. However, the design of open poultry houses, which utilizes evenly gapped wood slat flooring, compounds the problem of automatically distinguishing new droppings from dirty flooring. In our work, data consisting of chicken dropping images from a poultry farm in Malaysia were collected for analysis. We used the YOLOv5n algorithm for detecting chicken droppings and distinguishing between healthy and sick chickens based on observable features such as the colour and shape of their droppings. Our proposed architecture, which used the YOLOv5n algorithm, can accurately detect chicken droppings and classify them into three health classes (coccidiosis, healthy, and other unhealthy), with an accuracy rate of up to 94.9%. By leveraging advanced computer vision techniques, poultry farmers can benefit from timely and accurate health assessments, leading to improved productivity and animal welfare in open house poultry farming systems
Editorial Preview for June 2025 Issue
No full textEffective from Volume 3, JIWE has transitioned to a triannual publication release effort. Specifically, releases occur each February, June, and October. This June 2025 release contains 28 papers within the regular section that covers a broad range of application concerning Machine Learning (ML), Augmented Intelligence, Artificial Intelligence (AI), Generative AI, Data Mining (DM), Software Engineering, Sentiment Analysis, Recommender Systems, Healthcare, and other key areas in web engineering. Additionally, this edition presents a captivating collection of 6 papers curated by our Thematic Editor, Assoc. Prof. Dr. Heru Agus Santoso, under the theme "Augmented Intelligence". In his editorial, Assoc. Prof. Dr. Heru Agus Santoso highlights cutting-edge research on the integration of software engineering and business intelligence to support knowledge-driven strategies for competitive advantage. Moreover, these papers are aligned to some of the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) through advancements in healthcare technologies, SDG 9 (Industry, Innovation, and Infrastructure) through software and systems innovation research, and SDG 16 (Peace, Justice, and Strong Institutions) through contributions to privacy and cybersecurity research
Editorial Preview for October 2025 Issue
No full textThe October 2025 issue of the Journal of Informatics and Web Engineering (JIWE) concludes the journal’s triannual publication cycle and precedes its transition to a quarterly schedule commencing in 2026. This issue presents 21 research papers within its regular section, covering a wide range of areas including Artificial Intelligence (AI), Machine Learning (ML), Software Engineering, Recommender Systems, Cybersecurity, and Web Technologies. The collection reflects JIWE’s ongoing effort to advance research in the informatics and web engineering domain. A special thematic section, guest-edited by Prof. Ts. Dr. Hairulnizam Bin Mahdin, centers on “AI-Enhanced Computing and Digital Transformation”. The featured studies examine the role of AI in driving organizational efficiency, innovation, and sustainable digital growth. Several papers in this issue also align with the United Nations Sustainable Development Goals (SDGs)—notably SDG 8 (Decent Work and Economic Growth) through studies on intelligent automation, SDG 9 (Industry, Innovation, and Infrastructure) via research on digital platforms and system design, and SDG 11 (Sustainable Cities and Communities) through innovations supporting smart and sustainable urban development. Beginning in January 2026, JIWE will adopt a quarterly publication schedule (January, April, July, and October), reflecting the journal’s steady expansion and continued commitment to disseminating rigorous, high-impact research in informatics and web engineering
Unveiling the Pathways: Exploring Influential Factors Shaping the Intentions to Engage with ChatGPT: DOI: https://doi.org/10.33093/ijomfa.2025.6.1.1
No full textArtificial intelligence (AI), notably embodied in ChatGPT, has transformed various sectors, including education and content creation. Initially conceptualized in the 1950s, AI’s integration into education has facilitated personalized learning and dynamic evaluations. ChatGPT, an AI language model, exhibits advancements in natural language processing, aiding users in generating custom content. ChatGPT has rapidly gained popularity, reaching over 100 million monthly users. Its educational potential lies in providing tailored learning experiences and streamlining administrative tasks. However, there is a lack of research in the literature on factors influencing the use of ChatGPT among university students in Malaysia for educational purposes. Therefore, the objective of this review paper is to explore the factors that contributed to the intention to use ChatGPT which include academic content creation, information seeking, novelty, convenience, perceived usefulness and perceived ease of use. This study will use quantitative research methodology and a questionnaire will be created for the target respondents, the undergraduate students at Multimedia University at the Cyberjaya campus. A minimum of 109 responses will be collected for this study and the data will be processed for data analysis, using the SPSS software to analyze the collected data. The intention to ChatGPT among undergraduate students at MMU Cyberjaya was notably impacted by academic content creation, information seeking, convenience and perceived usefulness. In contrast, novelty and perceived ease of use did not exhibit significant influence. In summary, this study aims to provide valuable insights into the factors influencing the use of ChatGPT among university students in MMU Cyberjaya, offering significant implications for academics, researchers, policymakers, and AI developers. The outcome of this study holds significance for academics, researchers, policymakers and AI developers, contributing to their understanding of how individuals engage with and derive meaning from ChatGPT software in the education sector. The study emphasizes how difficult it is for schools to adapt to new technologies and how crucial it is to address a variety of issues in order to do so successfully. When integrating ChatGPT and other educational technologies, executives, education leaders, and other stakeholders should consider these factors. Policymakers could create policies addressing privacy and security issues, and educational institutions should build security, usability, and practicality into their digital plans
Generative Artificial Intelligence and Its Role in Shaping Customer Loyalty in Banking: A Conceptual Framework: DOI: https://doi.org/10.33093/ijomfa.2025.6.2.13
No full textThe role of Generative Artificial Intelligence (Generative AI) in Electronic Customer Relationship Management (Electronic-CRM) systems is reshaping consumer engagement in the banking or financial industry. There is a significant gap in understanding the direct impact of Generative AI on Electronic-CRM and customer loyalty in the banking industry. This paper proposes a conceptual framework to investigate the influence of Generative AI on enhancing Electronic-CRM, particularly in three key dimensions: data security, problem-solving, and customer orientation, and its ultimate impact on customer loyalty in the banking and financial sectors. An extensive literature review suggests that the role of Generative AI enhances data security measures, as data security remains a crucial concern in the banking industry. By addressing customer issues efficiently, streamlining queries, and reducing response times, Generative AI enables customer-oriented strategies that foster a stronger relationship between the bank and its customers. Data is compiled from databases such as Scopus, Google Scholar, and ScienceDirect to conceptualize and critically evaluate the customer’s long-term relationship after the incorporation of AI-driven CRM systems, particularly the role of Generative AI. Future empirical research will employ a quantitative methodology using Structural Equation Modelling (SEM) via Partial Least Squares (PLS) path modelling to validate the relationship and expand the proposed framework. Furthermore, the study will provide valuable insights for banking executives, policymakers, decision-makers, and technology developers on the role of Generative AI and Electronic-CRM in the banking business
Service quality integration – a case study of Qiandao Lake Scenic Spot, China
No full textIn the Yangtze River Delta region of China, the Qiandao Lake Scenic Spot stands as a 5A-rated tourist attraction, both nationally and internationally recognized. However, despite its acclaim, the service quality at this destination has not yet reached satisfactory levels, particularly in light of the significant increase in tourists after the COVID-19 pandemic. Furthermore, the existing service quality dimensions fail to adequately address a crucial aspect: cultural integration for enhancing customer satisfaction. Consequently, this study aims to comprehensively explore service quality integration at the Qiandao Lake Scenic Spot. We developed a six-dimensional Service Quality scale, which was then administered to tourists visiting the destination. An Expectation-Performance Analysis revealed that the largest gap between expectations and actual performance lies in the ‘assurance’ dimension, closely followed by ‘tangibility.’ This highlights tourists’ heightened concern for both ‘assurance’ and ‘tangibility’ aspects
Analysing consumer preference factors and their impact on willingness in vehicle leasing in Malaysia
No full textVehicle leasing is less developed in Malaysia than in Western countries due to the easy availability of loans for vehicle ownership and the underdeveloped public transport system. This study analysed the factors affecting consumer preferences and willingness to lease vehicles in Malaysia, including cost, convenience, flexibility, and ownership. Data was obtained through distributing 350 questionnaire copies, which yielded 257 responses, after which data was analysed with the help of SmartPLS4 and SEM. The study found that convenience, cost, flexibility, and ownership positively influence consumers' willingness to lease a vehicle. To encourage vehicle leasing, leasing establishments in Malaysia should prioritise these factors. This study is useful for professionals in the Malaysian vehicle leasing market and contributes to the literature on consumer willingness to lease vehicles in Malaysia
Optimization of Mechanical Properties in Nypa fruticans Composite Boards with Varying Additive Loadings
No full textThe optimisation of mechanical properties in composite materials is essential for advancing sustainable material utilisation of non-wood fibres, which often exhibit inferior mechanical performance compared to conventional wood-based composite boards. This study investigates the influence of varying nano-titanium dioxide (TiO2) loadings on the mechanical performance of Nypa fruticans-based composite boards. Epoxy resin was employed as the binding matrix, with nano-TiO2 incorporated at loading levels of 0%, 1%, 3%, 5% and 7% by weight. Key mechanical properties were evaluated through modulus of rupture (MOR), modulus of elasticity (MOE), and tensile strength testing. The results revealed a pronounced effect of nano-TiO2 incorporation on the composite’s mechanical performance, with improvements observed up to an optimal loading of 3 wt%. Beyond this critical threshold, the reinforcing efficiency of the nanoparticle declined, primarily due to agglomeration. This phenomenon was substantiated by scanning electron microscopy (SEM), which confirmed the microstructural changes and non-uniform nanoparticle distribution at higher loadings. Overall, the optimised composite board containing 3 wt% nano-TiO2 satisfied the ISO and ASTM standard requirements for both bending and tensile strength, demonstrating the viability of Nypa fruticans fibre as a sustainable alternative material for indoor application