JOURNAL INFORMATION AND TECHNOLOGY MANAGEMENT (JISTM)

DEFECT ENHANCEMENT DETECTION METHOD IN COMPLEX BACKGROUNDS BASED ON MIXED ATTENTION

Cheng Changyin — 2026-03-01

This paper proposes a defect enhancement detection method based on Mixed attention, aiming to address the difficulty of feature extraction caused by complex background interference in metal surface defect detection. The core of this method is the design of a lightweight Mixed Attention Module (MAM). This module integrates channel attention and spatial attention mechanisms in parallel, working collaboratively at multiple levels: channel attention adaptively recalibrates channel feature responses by modeling the interdependencies between feature channels, emphasizing feature maps related to defects; spatial attention focuses on key spatial locations in the feature maps, generating spatial weight masks to highlight defect regions and suppress texture and noise interference from irrelevant backgrounds. Simultaneously, the module employs an efficient structural design, achieving effective capture and fusion of multi-scale contextual information without introducing significant computational overhead, thereby enhancing the discriminative representation of defect features in complex backgrounds. Experimental results demonstrate that this method achieves significant improvements in detection accuracy (mAP) on the NEU-DET and GDUT-DET public metal surface defect datasets.

ANALISIS PERAMALAN ARAH ALIRAN KADAR KEMISKINAN MENGIKUT NEGERI DI MALAYSIA SERTA FAKTOR MENYUMBANG KEPADA KEMISKINAN

Jia En Tan — 2026-03-30

Pandemik COVID-19 telah meningkatkan kadar kemiskinan di Malaysia. Kajian ini meramalkan kadar kemiskinan mengikut negeri bagi tempoh 2023 hingga 2032 dengan menumpukan kepada tiga dimensi utama iaitu kemiskinan mutlak, tegar dan relatif. Data sekunder diperoleh daripada Jabatan Perangkaan Malaysia menjangkau dari tahun 1970 hingga tahun 2022. Kaedah gabungan digunakan di mana kaedah pelicinan eksponen untuk ramalan siri masa dan analisis awan perkataan untuk analisis kualitatif. Dapatan kajian menunjukkan bahawa Sabah dijangka mempunyai kadar kemiskinan mutlak tertinggi pada tempoh awal ramalan, namun dijangka akan diatasi oleh Perlis selepas tahun 2028. Negeri Kelantan dan Sarawak pula masing-masing diramal mempunyai kadar kemiskinan tegar dan relatif tertinggi pada masa depan. Ini disebabkan oleh jurang pembangunan wilayah, kekurangan tahap infrastruktur asas, dan kebergantungan terhadap sektor pertanian berkaedah tradisional. Kajian mencadangkan pelaksanaan dasar bersasar mengikut keperluan negeri termasuk pelaburan dalam infrastruktur luar bandar, pemodenan sektor ekonomi tempatan, pembangunan kapasiti dan kemahiran untuk menangani kemiskinan secara mampan. Diharap supaya kajian ini dapat menjadi rujukan penting kepada pembuat dasar dan penyelidik dalam merangka strategi pengurangan kemiskinan jangka panjang yang lebih inklusif dan berkesan menjelang 2032.

The COVID-19 pandemic has increased poverty rates in Malaysia. This study forecasts poverty rates by state for the period 2023 to 2032 by focusing on three main dimensions, namely absolute, hardcore and relative poverty. Secondary data was obtained from the Department of Statistics Malaysia spanning from 1970 to 2022. A combined method was used where exponential smoothing method for time series forecasting and word cloud analysis for qualitative analysis. The study findings show that the state of Sabah is expected to have the highest absolute poverty rate in the early forecast period, but is expected to be surpassed by Perlis after 2028. Kelantan and Sarawak are respectively predicted to have the highest hardcore and relative poverty rates in the future. This is due to regional development gaps, lack of basic infrastructure, and reliance on traditional agriculture sectors. The study recommends the implementation of targeted policies according to state needs including investment in rural infrastructure, modernization of local economic sectors, capacity and skills development to address poverty sustainably. It is hoped that this study can serve as an important reference for policymakers and researchers in formulating a more inclusive and effective long-term poverty reduction strategy by 2032.

THRESHOLD IMPROVEMENT USING THE OTSU METHOD WITH HISTOGRAM ON PADDY IMAGES

Lukman Hakim — 2026-03-11

Accurate image segmentation methods play a very important role in improving paddy grain classification performance. One of the segmentation stages is determining the threshold, usually using classic Otsu. This method is widely used in the segmentation process, and the instability of the threshold in classic Otsu in uneven lighting and complex histogram distribution. This study proposes an improvement to the Otsu-based segmentation method by combining histogram normalization and trigonometric variance modulation, namely Otsu with normalization, Otsu Sine, and Otsu Tangent. The proposed methods were evaluated using Random Forest as a classifier and texture features with GLCM. Experimental results show that the Otsu Sine method achieves the best performance among the other methods, with an accuracy of 0.92, precision of 0.93, recall of 0.93, and F1 score of 0.93. Five-fold cross-validation yields superior results for Otsu-Sine, with the highest average accuracy (94.83%) among all methods. Further pairwise tests showed that the improvement in performance compared to classic Otsu was statistically significant (p = 0.04). Threshold stability analysis showed that Otsu-Sine maintained low variance while adapting effectively to changes in intensity distribution, whereas Otsu-Tangent showed high instability.

DRIVERS OF WAREHOUSE MANAGEMENT SYSTEM (WMS) ADOPTION AND ITS EFFECTS ON DISTRIBUTION CAPABILITY AND OPERATIONAL PERFORMANCE

Hazwani Idris — 2026-03-31

The logistics and supply chain sector in Malaysia is experiencing rapid transformation driven by digitalisation, e-commerce expansion, and increasing customer expectations for faster, more reliable delivery services. In this context, Warehouse Management Systems (WMS) have become an important technological enabler for improving warehouse efficiency, inventory visibility, and distribution processes. However, WMS adoption among Malaysian logistics firms, particularly small and medium-sized enterprises (SMEs), remains uneven due to technological, organisational, and financial constraints. Drawing on the Diffusion of Innovations (DOI) theory, this conceptual paper develops a framework to explain the drivers of WMS adoption and its proposed implications for distribution capability and operational performance. Specifically, the paper synthesises the five DOI attributes—relative advantage, compatibility, complexity, trialability, and observability—as key antecedents of WMS adoption. It further positions distribution capability as an important mechanism through which WMS adoption may enhance operational performance. By integrating innovation adoption theory with logistics capability literature, this paper offers a structured conceptual foundation for understanding how WMS adoption can contribute to operational improvement in Malaysian logistics firms. The paper contributes to the literature by extending DOI to the context of warehouse digitalisation and by proposing a theoretically grounded model that can guide future empirical research and managerial decision-making related to logistics digital transformation.

AN ENHANCED DARK CHANNEL PRIOR WITH ITERATIVE TRANSMISSION UPDATE FOR SINGLE

You Qin — 2026-03-01

Hazy images suffer from visibility degradation and colour distortion due to light scattering and atmospheric attenuation, which complicates downstream vision tasks and human interpretation. The Dark Channel Prior (DCP) remains a simple yet effective physically grounded method for single image dehazing; however, it tends to underestimate transmission in bright or textureless regions (e.g., sky or specular surfaces), leading to halo artefacts and colour distortions. To address this, this paper introduces a physically interpretable corrective term from the DCP-derived transmission–radiance pair to compensate for local violations of the dark-channel assumption. The proposed method iteratively refines both the transmission map and scene radiance without requiring region segmentation. Experiments on the RESIDE and NH-HAZE datasets demonstrate that our method achieves superior PSNR, SSIM, and colour fidelity than four representative model-based dehazing algorithms, while maintaining competitive computational efficiency. Overall, the proposed iterative refinement substantially enhances the robustness and visual quality of traditional prior-based dehazing while avoiding the computational burden and data dependency of deep learning approaches.

FROM CONNECTION TO COLLABORATION: HOW SOCIAL MEDIA TRANSFORMS WORKPLACE COMMUNICATION IN SMES- A SYSTEMATIC LITERATURE REVIEW

Mohammed Hashim Abdulkareem Al-Sharaa — 2026-03-30

The increasing integration of social media into organizational contexts has transformed how employees communicate and collaborate in the workplace. Despite growing interest in this area, limited research has systematically examined how social media–enabled workplace communication evolves into collaborative practices within small and medium-sized enterprises (SMEs). This study aims to examine how social media transforms workplace communication into collaboration in SMEs through a systematic literature review. A total of 36 peer-reviewed journal articles published between 2010 and 2024 were analyzed using a thematic analysis approach to identify recurring patterns and mechanisms reported in the literature. The results reveal five dominant themes: (1) social media as an enabler of informal and interactive workplace communication, (2) the transition from connection-based communication to collaborative work practices, (3) social media–enabled knowledge sharing and collective learning, (4) organizational and individual drivers shaping social media–based collaboration, and (5) tensions and contradictory effects associated with social media use in SME contexts. These findings indicate that social media functions not merely as a communication tool but as a collaborative infrastructure embedded within everyday work practices. The study contributes to the information systems and organizational communication literature by providing an integrated framework that explains the transition from connection to collaboration in SMEs and offers practical insights for managers seeking to leverage social media for collaborative work.

THE DIGITAL CONSUMER: A SCOPUS AI-BASED REVIEW OF ONLINE SHOPPING BEHAVIOR

Fatihah Norazami Abdullah — 2026-03-11

E-commerce does not stand still, and neither do consumers. As online shopping becomes more common, decisions are shaped by more than speed or convenience. Concerns about security, uneven digital skills, and the growing use of AI now sit at the center of how people shop online, sometimes encouraging participation and sometimes holding it back. This study examines digital consumer behavior by looking across four connected areas: the virtual marketplace, consumer attitudes, data analytics, and predictive modelling. Drawing on a systematic review and thematic analysis of Scopus-indexed studies, it brings together research that is often discussed separately. The review shows that AI-driven personalization, especially through recommendations and targeted promotions, can improve engagement and customer satisfaction, and may contribute to repeat purchasing over time. However, these benefits are not universal. Trust remains fragile. Security and privacy concerns continue to limit adoption, particularly in emerging economies where regulatory protection and digital literacy are still developing. While digital transformation has improved access and usability, predictive modelling reveals how deeply machine learning has become embedded in e-commerce decision-making rather than functioning as a background tool. The findings suggest that firms cannot rely on personalization alone. Trust-building measures and transparent data practices are just as critical. Policymakers also have a role to play, particularly in strengthening transaction security and supporting consumer digital skills. Future research should pay closer attention to how demographic and cultural differences shape responses to AI-enabled shopping. Overall, this study offers a grounded view of the digital consumer landscape and highlights practical directions for improving online engagement and satisfaction.

ADVANCEMENT IN CRIMINAL IDENTIFICATION FOR ENHANCED PUBLIC SAFETY: SVM-BASED FACE RECOGNITION WITH VGG ARCHITECTURE

Zainab Othman — 2026-03-31

Face recognition technology helps Malaysia’s Royal Military Police (RMP) identify criminals faster. Manual identification at roadblocks makes errors and wastes time. Criminal activities are getting worse, but current identification systems do not work well enough. Better criminal identification systems have become necessary for police work. This research presents a Criminal Face Recognition System that identifies criminal faces using accurate image matching. The study improves public safety and supports RMP operations. Deep learning methods power the system, combining Support Vector Machine (SVM) with Visual Geometry Group (VGG) architecture. Test results show 93.50% accuracy, proving the system works well for recognizing known criminals and its strength when processing new faces. These technological advancement puts the RMP ahead in using new technology, showing their commitment to public safety and security. Installing such systems fixes current identification problems while giving police reliable tools for catching criminals. Police need modern solutions that work against changing criminal methods and this system provides those tools.

THE DEVELOPMENT OF A MULTI-LAYER PERCEPTRON MODEL TO PREDICT THE TOURIST ARRIVAL IN MALAYSIA DURING POST-COVID-19 BASED ON ECONOMIC INDICATORS

Muhammad Hanif Othman — 2026-03-30

Tourism plays a vital role in Malaysia’s economy, contributing significantly to GDP, employment, and infrastructure development. However, the post-COVID-19 pandemic caused a severe decline in tourist arrivals, posing challenges for the industry’s recovery. In this regard, this study develops a prediction model for forecasting tourist arrivals in Malaysia using a Multi-Layer Perceptron with Backpropagation (MLP-BP) Learning Approach by considering economic indicators, namely Gross Domestic Product (GDP), inflation rate, and exchange rates. The study utilizes historical data from the Ministry of Tourism, Arts, and Culture (MOTAC) and the Department of Statistics Malaysia (DOSM), covering pre-pandemic and post-pandemic periods. Experimental results demonstrate that the MLP with 2 hidden layers demonstrated the best validation performance with a positive correlation coefficient of 0.3685. It is found that an increase in the exchange rate, higher GDP per capita, and lower inflation may attract more visitors. Consequently, this research contributes valuable insight to the development of data-driven policies for Malaysia’s strategic tourism sector.

EXPLORING THE INTERSECTION OF IMMERSIVE VR/AR TECHNOLOGIES AND CULTURAL IDENTITY IN CHINESE VARIETY SHOWS

Guo Zhiyao — 2026-03-02

This paper investigates the impact of VR/AR immersive variety shows on the creation of organizational cultural identity based on the cultural identity theory. In particular, it explores how the defining characteristics of immersive media, such as embodiment, interactivity, and cultural symbolism, support the identity-forming process.To determine the implications, this study integration approach will be based on the semi-structured interviews, with the participants being the producers, performers, and viewers; participant observation, analysis of program materials, and the analysis of discussions among audiences in social media platforms. Three exemplary cases are chosen to be analyzed VR headset-based formats, mobile AR formats, and live hybrid formats. The inherent design of the comparative will facilitate cross-case development and theory development.Initial therapies indicate that audiences will have a greater emotional connection with content that is rich in cultural elements. The embodied experiences are better at improving memorability of cultural symbols and the meaning-making processes depend on both the viewing context and the composition of the audience. The paper is designed to make its way into the media identity theory and provide practical implications to the content design and audience engagement approaches in the fast-changing XR environment.

EXPLORING THE FRONTIERS OF GAMIFICATION: A BIBLIOMETRIC ANALYSIS OF RESEARCH TRENDS AND INFLUENCES

Yanyan Zhang — 2026-03-31

Gamification has become popular in industries. However, its presence in healthcare remains in the early stages. This study conducts a bibliometric analysis to explore gamification in a broad sense and its potential in healthcare. A bibliometric and network analysis (2011–2023) based on 5,240 papers from 8,722 scholars in the Scopus database identifies key scholars and influential articles. Publications are analysed by source title, citations, country, and institution. The results indicate that gamification is less applied in healthcare than in education and other fields. Bibliometric networks, such as co-occurrence networks of keywords and authors, are examined. A cluster-based content analysis further explores gamification in healthcare. Findings demonstrate a growing academic interest in gamification, with key research topics focusing on motivations for implementation, learning, healthcare, well-being, and social networks. The study highlights research gaps, particularly in linking gamification to healthcare. To address this, a multilevel framework is proposed to guide future research on gamification in healthcare.

ARTIFICIAL INTELLIGENCE IN CHINA'S RETAIL INDUSTRY: A SYSTEMATIC LITERATURE REVIEW

Zhang Haidong — 2026-03-15

Artificial Intelligence (AI) has emerged as a transformative force in China's retail industry, enabling intelligent decision-making across demand prediction, dynamic pricing, supply chain optimization, and consumer behavior analysis. Despite the growing body of research, existing studies remain fragmented across diverse literature sources, lacking a structured and unbiased review framework. This fragmentation limits the ability to systematically assess AI applications for operational efficiency, resilience, and sustainability in China's retail sector, making it difficult for researchers and practitioners to identify best practices and prioritize high-impact AI solutions. To address this critical gap, this study makes two primary contributions. First, we develop a comprehensive systematic review methodology tailored to the field of AI in retailing, drawing on 450 peer-reviewed articles published between 2015 and 2025, sourced from the Web of Science Core Collection. Second, leveraging this methodology, we categorize prevalent AI techniques including machine learning, deep learning, reinforcement learning, and natural language processing. We then map these techniques to their practical applications within retail operations across the dimensions of efficiency, resilience, and sustainability. Furthermore, we identify critical research gaps and propose promising directions for future investigation. The proposed review framework and novel classification scheme provide a structured foundation for future empirical research and guide industry adoption of AI strategies in China's rapidly evolving retail landscape.

SRIVIJAYAPEDIA: A DIGITAL PLATFORM FOR CULTURAL HERITAGE PRESERVATION OF THE SRIVIJAYA CIVILIZATION

Nazlena Mohamad Ali — 2026-03-01

This paper introduces Srivijayapedia, a digital platform developed to preserve and promote the cultural heritage of the Srivijaya civilization. Documenting historical environments is essential for enhancing public understanding of the past and reinforcing individual and national identities. Such knowledge fosters cultural unity, instills pride, and aids in sustainable heritage preservation. The key question posed is how a digital encyclopedia can enhance public engagement with heritage, enabling individuals to explore their identity and understand historical evolution. Although abundant historical information on Srivijaya exists, it remains scattered and underutilized in digital formats. Digital tools, such as virtual reconstructions and interactive platforms, offer accessible and immersive ways to preserve and disseminate cultural knowledge. Srivijayapedia is an interdisciplinary project rooted in Human-Computer Interaction (HCI), aiming to transform traditional historical documentation into an engaging digital experience. A thematic literature review guides the focus on three core areas: (1) the educational impact of digital heritage platforms, (2) the current state of Srivijaya's digital representation, and (3) the economic potential of digital heritage in promoting tourism. The platform embraces intercultural and community-based approaches to ensure inclusivity and sustainability. Ultimately, Srivijayapedia contributes to broader efforts aligned with the United Nations Sustainable Development Goals (SDGs), focusing on education, cultural sustainability, innovation, and economic development.

BENIGN-AWARE HISTOGRAM GRADIENT BOOSTING FOR MALICIOUS IOT NETWORK TRAFFIC DETECTION

Mohd Noor Derahman — 2026-03-30

Detecting malicious traffic in Internet of Things (IoT) networks remains challenging because flow distributions are highly skewed, attack behaviours evolve quickly, and practical deployments must balance accuracy with computational cost. This study evaluates five classical machine learning models on IoT-23 and CICIoT2023 under multiple sample sizes and preprocessing settings. The experimental design includes 1,000, 5,000, 10,000, and 50,000-record subsets, median imputation, five-fold stratified cross-validation, explicit hyperparameter tuning, SMOTE-based imbalance analysis, and training and inference cost measurement. In addition to the five baseline models, the study introduces a benign-aware histogram gradient boosting variant (BA-HGB) that applies tuned cost- sensitive weighting to the minority benign class without synthetic data generation. On CICIoT2023, BA-HGB achieved the best five-fold macro- F1 score relative to the baseline models on the 10,000-sample benchmark (0.8898 +/- 0.0153), the best macro-F1 at 50,000 samples (0.8996 +/- 0.0038), and the highest ROC-AUC (0.9971 +/- 0.0003). An ablation in- side the HGB family further showed that all HGB variants outperformed the RF and GB baselines, whereas SMOTE consistently reduced both macro-F1 and benign-class F1. These results support the generalizability of the findings and show that histogram-based boosting is a strong practical direction for IoT intrusion detection, while imbalance handling mainly changes the accuracy-stability trade-off within that family.

CONCEPTUAL DESIGN PRINCIPLES FOR VISUALIZATION-ENHANCED DAO GOVERNANCE IN COOPERATIVES

Roslan Abdul Wahab — 2026-03-15

Cooperatives continuously faced governance challenges related to transparency, accountability, and member participation as decision-making processes became more complex. Hence, it was proposed that blockchain-based Decentralized Autonomous Organizations (DAOs) could serve as a governance mechanism. Despite this potential, DAO governance systems remained difficult for many cooperative members to trust, adopt, and interpret. This is even more so when the governance processes involve technically complex blockchain information. Therefore, this study aims to develop a set of conceptual design principles to explain how visualization can support trustworthy DAO governance in a cooperative. This study adopted a design-oriented conceptual approach. Focusing on Cognitive Fit Theory and Trust Theory, and current research on blockchain governance and cooperative decision-making, this paper depicts how visualization functions as a cognitive mechanism that drives members’ understanding of governance processes and outcomes. The analysis identified six key principles, which included emphasized interpretability over technical completeness, cognitive load reduction, process visibility, inclusivity, and trust support in visualization-based DAO governance. These principles highlighted that transparency in blockchain was not achieved only through data availability, but via visual presentation of governance information in forms that align with users’ cognitive processing capabilities. This paper contributed to the body of knowledge involving digital governance and blockchain adoption by offering theory-informed design knowledge that extends beyond the technology acceptance model. The proposed design principles provide a foundation for future research and offer practical guidance for organizations and system developers in supporting inclusive, understandable, and trustworthy DAO-based governance in cooperatives.

PERFORMANCE EVALUATION OF OPEN-SOURCE WEB VULNERABILITY SCANNERS FOR SQL INJECTION DETECTION IN WEB APPLICATIONS

Raihana Md Saidi — 2026-03-31

Small and Medium Enterprises (SMEs) are embracing online platforms which results in becoming more exposed to cybersecurity threats, especially SQL Injection (SQLi) attacks. SQLi attack is among the most common web application vulnerabilities listed by OWASP. To address these risks, this research examines the performance of several open-source Web Vulnerability Scanners (WVS) in detecting SQLi threats. Four popular open-source scanners: OWASP ZAP, SQLmap, Skipfish and Wapiti have been tested across five types of web applications. Vulnerable testbeds such as DVWA and OWASP Mutillidae II and the real e-commerce sites have been tested to see how the tools perform in practical situations. All the scanners have been evaluated based on few parameters: detection rate, response time, resource usage on CPU and memory and ease of use. The scanners show variations of results during the experiment. These findings offer useful guidance for SMEs in choosing cost-effective and efficient scanning tools that match their cybersecurity needs.

GIS APPLICATIONS FOR HALAL LOGISTICS COMPLIANCE MONITORING: A CRITICAL REVIEW OF SPATIAL GOVERNANCE FRAMEWORKS

Asrul Zakaria — 2026-03-01

The rapid expansion of global halal markets has intensified the need for robust logistics systems to safeguard halal integrity across increasingly complex, spatially distributed supply chains. Within this context, Geographical Information Systems (GIS) have emerged as a promising digital enabler for compliance monitoring, traceability, and governance by embedding logistics operations within explicit spatial and temporal frameworks. However, despite growing scholarly interest, the application of GIS in halal logistics remains fragmented, technologically focused, and insufficiently integrated with broader halal governance and regulatory mechanisms. Existing halal logistics practices continue to rely heavily on periodic audits, document-based reporting, and siloed information systems, limiting real-time visibility and proactive risk management. The literature further reveals a lack of a coherent synthesis of how GIS capabilities, such as spatial traceability, geofencing, risk mapping, and decision-support, can be systematically aligned with halal compliance requirements across transportation, warehousing, and distribution stages. This fragmentation highlights a clear research gap at the intersection of geospatial technologies, halal standards, and supply chain governance. Accordingly, this review aims to critically synthesize and evaluate existing studies on GIS applications in halal logistics compliance and monitoring, with particular emphasis on transparency, traceability, and trust as core governance dimensions. By integrating insights from logistics management, geospatial science, and halal studies, the review consolidates dispersed conceptual discussions into a structured understanding of GIS as a spatial governance enabler rather than a standalone technical tool. The review contributes to the literature by clarifying conceptual linkages, identifying gaps in governance and implementation, and outlining future research directions for spatially enabled halal logistics systems. It concludes that GIS holds significant potential to support evidence-based, continuous, and location-aware compliance monitoring when embedded within coherent institutional and regulatory frameworks. This review is timely and important as halal supply chains undergo digital transformation, offering scholars and policymakers a consolidated foundation for advancing resilient, transparent, and trusted halal logistics governance.

PORTAL WEB INTERAKTIF UNTUK PENGURUSAN MAKLUMAT DAN JARINGAN PROFESIONAL BAGI KOMUNITI PEMUZIK TEMPATAN (MUSICHUB)

Syaimak Abdul Shukor — 2026-03-30

Industri muzik tempatan menghadapi cabaran signifikan dalam ekosistem pekerjaan dan pembangunan kerjaya. Pemuzik, khususnya yang bekerja secara bebas, sering kali bergelut dengan masalah akses yang terhad dan tidak sistematik kepada peluang pekerjaan dan kolaborasi profesional. Menyedari isu ini, kajian ini memperkenalkan MusicianHub, sebuah portal web interaktif yang dibangunkan untuk menyediakan platform terpusat bagi pemuzik, sekali gus memperkemaskan proses carian peluang dan pengurusan kerjaya. Objektif utama projek ini adalah untuk membangunkan sebuah sistem maklumat berasaskan web yang berfungsi sebagai hab maklumat dan jaringan sosial profesional. Metodologi pembangunan melibatkan penggunaan perisian Dreamweaver dan pangkalan data phpMyAdmin untuk menyimpan dan mengurus data pengguna, peluang pekerjaan, dan aktiviti pentadbiran. Sistem ini direka bentuk untuk menyokong ciri-ciri utama seperti pengurusan pekerjaan (menambah, menyenarai, mencari, dan memohon pekerjaan), profil pengguna yang dinamik, serta hierarki akses bagi pentadbir dan pengguna biasa. Hasil projek ini adalah portal yang efisien dan mesra pengguna yang dapat menghubungkan pemuzik dengan peluang profesional yang relevan dan pada masa yang sama, menyediakan saluran yang berkesan bagi organisasi untuk mendapatkan bakat yang sesuai. Kajian ini menyumbang kepada bidang sistem maklumat dan pengurusan teknologi dengan menunjukkan bagaimana teknologi digital boleh dimanfaatkan untuk menyokong dan memperkasakan industri kreatif, terutamanya dalam konteks pasaran yang dinamik. Secara tidak langsung, MusicianHub berpotensi untuk memberi impak positif terhadap dasar sokongan industri muzik, meningkatkan peluang ekonomi, dan memperkukuh kedudukan pemuzik tempatan dalam era digital.

The local music industry faces significant challenges in its employment ecosystem and career development. Musicians, particularly freelancers, often struggle with limited and unsystematic access to professional job opportunities and collaborations. Recognising this issue, this study introduces MusicianHub, an interactive web portal developed to provide a centralised platform for musicians, thereby streamlining the process of opportunity discovery and career management. The main objective of this project is to develop a web-based information system that serves as an information hub and professional social network. The development methodology uses Dreamweaver software and a phpMyAdmin database to store and manage user data, job opportunities, and administrative activities. The system is designed to support key features such as job management (adding, listing, searching, and applying for jobs), dynamic user profiles, and an access hierarchy for administrators and regular users. The outcome of this project is an efficient and user-friendly portal that connects musicians with relevant professional opportunities while providing an effective channel for organisations to find suitable talent. This study contributes to information systems and technology management by demonstrating how digital technology can support and empower the creative industry, especially in a dynamic market. Indirectly, MusicianHub has the potential to positively impact music industry support policies, increase economic opportunities, and strengthen the position of local musicians in the digital era.

ENHANCED CAESAR CIPHER ALGORITHM USING DYNAMIC KEY GENERATION WITH TIMESTAMP TECHNIQUE FOR DATA SECURITY

Muhammad Rifqi — 2026-03-11

Data security remains a critical concern in the digital era due to the rising frequency of unauthorized access. While the Caesar Cipher is a foundational cryptographic technique, its static nature makes it highly vulnerable to frequency analysis and brute-force attacks. This study proposes an Enhanced Caesar Cipher algorithm that integrates Dynamic Key Generation utilizing a timestamp technique to improve data confidentiality. By generating keys dynamically based on the exact time of encryption, the algorithm ensures that the same plaintext results in different ciphertexts at different intervals, effectively mitigating basic substitution pattern recognition.Experimental results demonstrate that the proposed method maintains a lightweight computational profile, achieving an average encryption time of 0.3 seconds for a 1 MB file with a linear scaling pattern . This efficiency makes the algorithm particularly suitable for low-power environments, IoT applications, and educational purposes where modern complex encryption may be resource-prohibitive. While not intended to replace high-security standards like AES, this enhanced approach provides a significant security improvement over the traditional Caesar Cipher by introducing temporal variability into the key space.

COMPARATIVE ANALYSIS OF METADATA IN IMAGE AND VIDEO FILES CAPTURED BY IOS AND ANDROID DEVICES

Hajar Izzati Mohd Ghazali — 2026-03-31

embedded in image and video files, focusing on the differences between iOS and Android platforms across various file formats. Metadata was extracted using three specialized tools: Metadata2go, ExifMeta, and ExifInfo. The research involved systematically retrieving files from both platforms, organizing them by format and operating system and conducting a comparative evaluation. Eight key metadata attributes were analyzed: device make, model, software version, timestamp, geographic coordinates (longitude and latitude), color space and lens model. Results indicate that iOS devices consistently retain more detailed and structured metadata than Android devices, reflecting architectural differences in metadata handling. This study underscores the critical role of metadata in digital forensics and information management.

AN IMPROVED MULTI-OBJECTIVE GREY WOLF OPTIMIZATION TASK ALLOCATION METHOD IN MOBILE CROWD SENSING FOR SMART AGRICULTURE

Liang Yan — 2026-03-30

Smart agriculture is the key to ensuring China's food and water resource security. However, the operation and maintenance efficiency of the irrigation canal network that spreads across farmlands is constrained by the traditional inefficient inspection methods. Mobile crowd sensing (MCS) offers a new approach for the inspection of farmland water channels. By mobilizing farmers' daily mobile resources, it is expected to achieve low-cost and wide-coverage monitoring. The core of its efficiency lies in the intelligent allocation of sensing tasks. However, most of the existing mobile crowd sensing methods are designed based on general scenarios and have not been fully adapted to the scene characteristics of farmland water channel detection, such as "numerous points, long lines, complex terrain, and hidden problems", making it difficult to effectively coordinate and optimize the two mutually restrictive core goals of platform benefits and total costs for farmers. To solve these problems, this paper proposed a multi-objective optimization task allocation model in mobile crowd sensing for the inspection of smart agricultural canals. In order to solve the proposed model, we designed an improved multi-objective grey wolf optimization algorithm (IMOGWO-SA/D) that integrates simulated annealing (SA) mechanism and decomposition strategy. This algorithm collaboratively optimizes multiple single-objective sub-problems through Chebyshev decomposition and enhances the global search ability with the aid of SA mechanism, thereby effectively balancing convergence and the distribution of solution sets. Through simulation experiments and comparisons with traditional benchmark algorithms, the experimental results show that the algorithm proposed in this paper has significant advantages in convergence, diversity of solution sets and scene adaptability. This article provides innovative methods and technical solutions for building an efficient and low-cost distributed monitoring system for smart agriculture.

DIGITAL COMMUNICATION COMPETENCE AND EMOTIONAL RESILIENCE: A SYSTEMATIC REVIEW OF SOCIAL MEDIA LITERACY INTERVENTIONS FOR PREADOLESCENTS

Nur Haffiza Rahaman — 2026-03-02

The rapid integration of social media into preadolescents’ daily lives has redefined patterns of communication, learning, and emotional development. While digital platforms offer opportunities for connection and creativity, they also present risks to mental health, including cyberbullying, anxiety, and social comparison. These challenges underscore the urgent need to understand how digital communication competence and emotional resilience can serve as protective mechanisms that enhance psychological well-being. This study systematically reviews existing scholarship on social media literacy interventions targeting preadolescents, applying the PRISMA 2020 protocol to ensure transparency and replicability. Two major databases Scopus and Web of Science were systematically searched, yielding a total of 31 primary studies published between 2015 and 2025 that met inclusion criteria. Data were extracted, coded, and thematically synthesized. The analysis produced three significant thematic clusters: (1) Digital Communication Competence and Literacy Development (2) Emotional and Psychological Resilience among Adolescents, and (3) Social Media Interventions and Behavioral Outcomes. The review reveals that integrated interventions combining media literacy, emotional intelligence, and participatory communication yield the impacts on adolescents’ digital well-being. However, regional gaps persist, particularly in culturally grounded studies within Southeast Asia. The paper concludes that future research should advance a context-sensitive communication framework that unites digital competence and emotional resilience as twin pillars for promoting safe, ethical, and psychologically adaptive social media engagement among preadolescents.

EXTRA-VOCAL SOUND AND CONDUCTING GESTURE IN CLOUDBURST: A CONDUCTING ANALYTICAL PERSPECTIVE

Yi1 Huang — 2026-03-31

This paper aims to analyse the conducting gestures and techniques required to realise extra-vocal sound in Cloudburst, a widely performed work by Eric Whitacre in the contemporary choral repertoire. Rather than relying solely on pitched singing, Cloudburst incorporates air sounds, body percussion, indeterminate vocal noise, and spatialised sound actions to evoke natural phenomena such as wind, rain, and thunder. Using a conducting-analytical perspective, this study examines how these sound-generating actions function structurally and expressively and how conductors translate them into coherent musical outcomes through gesture design and rehearsal practice. The study employs three phases of inquiry: score analysis, rehearsal-based observation, and conducting reflection. Particular attention is given to conducting gestures, dynamic control, rhythmic coordination, and spatial organisation in rehearsal and performance contexts. The findings suggest that the integration of extra-vocal sound significantly expands choral expressivity while placing increased demands on the conductor’s physical communication, rehearsal planning, and interpretative decision-making. The study contributes practical conducting strategies grounded in professional practice, this paper offers pedagogical insights into contemporary choral conducting and performance practice.

EXAMINING THE MODERATING EFFECT OF EMPLOYEE READINESS ON TECHNOLOGY-DRIVEN PERFORMANCE IN THE MANUFACTURING SECTOR

Mehela Subramaniam — 2026-03-15

Research points that most technological development not innovative and this effect negatively on employee performance. New technological innovation will help employees to work effectively and increase their output dramatically. The aim of this study is to explore more information towards the technology innovation that influence the employee performance. This study examines the relationship between advanced robotics, artificial intelligence (AI) and electronic monitoring system with employee performance in manufacturing industry can be moderated by employee readiness. In addition, this research is intended to examine the relationship between several variables in a quantitative technique. In contrast, 384 respondents are the sample size. Therefore, this study seeks to gather at least 384 respondents from targeted respondents. The unit of research is individual. This research uses convenience sampling since it is readily accessible for the sample to be collected. To conduct this research, researcher use the primary data collection method to gather the data. The questionnaire method is recorded in this research instruments and measurement to collect quantitative data. Moreover, the data analysis is discussed in five studies such as frequency analysis, descriptive analysis, reliability analysis, Pearson correlation analysis and multiple regression analysis. There are six hypotheses tested in this study. In conclusion, there were only four accepted hypotheses and two rejected hypotheses.

INTEGRATION OF ARTIFICIAL INTELLIGENCE (AI) IN SHAPING USER INTERFACE (UI) AND USER EXPERIENCE (UX) IN MOBILE PACKAGING DESIGN: iDPAC APPS

Nurulkusuma Adnan — 2026-03-02

Intelligence Digital Packaging (iDPAC) is a digital integration of artificial intelligence (AI) in mobile applications' user interface and user experience (UX) design modules. This approach addresses the lack of educational resources and samples in packaging design, leading to unrealistic designs. The study focuses on AI in UI and UX mobile app prototypes and packaging design processes to optimize its advantages in the creative design industry. The qualitative methodology involves observation and interviews with educators, learners, and industry experts to gather insights on potential improvements in AI integration in UI and UX mobile apps. The study uses Design Developmental Research (DDR) and Design Thinking (DT) frameworks to discover user needs, design prototypes, and test them with real users. The DDR-DT framework has effectively facilitated the development of mobile apps that meet user satisfaction in packaging design.

AN EMPIRICAL ANALYSIS OF INTERNAL CONTROL GOVERNANCE IN ACCOUNTING INFORMATION SYSTEMS: USERS’ DILEMMAS, STRATEGIC TRADE-OFFS AND OPERATIONAL PRACTICES

Suziel Nchamaobari Obarieeh Epollo — 2026-03-30

The Accounting Information System (AIS) is designed to collect, record, store and process financial transactions and data, converting them into meaningful information to support business planning and operational control. The increasing reliance on AIS has strengthened concerns regarding the effectiveness of controls and the ethical behaviour of system users. Internal Control governance (ICG) is the mechanisms through which AIS controls are design that can influence on users’ operational processes and ethical decision-making. Thus, this study explores the dilemmas faced by AIS users within the scope of Control Governance and seeks to understand how organizations can alleviate stringent governance requirements without compromising system integrity. A qualitative research approach was employed, involving primary data collection from a multinational company operating in Malaysia with overseas headquarters. Informants were selected from key departments, namely Finance, Human Resources (HR)and Information Technology (IT). Using thematic analysis, the study uncovered that AIS users often face challenges in executing their routine responsibilities under the constraints imposed by ICG frameworks. The findings highlight several ethical and procedural dilemmas that influence AIS user performance, particularly in areas such as regulatory awareness, compliance obligations and procedures, organizational culture, reporting standards and overall productivity and efficiency.

ARTIFICIAL INTELLIGENCE – DRIVEN PERSONALIZATION: SYSTEMATIC REVIEW OF FEATURE INFLUENCE ON JAWI LITERACY MODEL FOR DYSLEXIA

Syamilah Saliman — 2026-03-15

The effectiveness of personalized educational technology is important for students with complex orthographic processing disorders. Considering the unique challenges posed by the Jawi script among dyslexic students, the objective of this research is to identify the artificial intelligence (AI) features that influence the development of personalized model for Jawi literacy among dyslexic students. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a Systematic Literature Review (SLR) was conducted using advanced search strategies across Scopus and Web of Science (WoS) databases. A total of 23 published papers were included. From these, 21 categories of grouped codes which were synthesized into five analytical themes reflecting the development of personalized models: Data Processing and Input Features, Core AI Architecture and Models, Adaptation and Personalization, Interface and Presentation, and Feedback, Fluency and Assessment. This research identifies five themes of AI features demonstrates for intervention development, concluding that successful personalization relies on the synergistic integration of multimodal feature extraction and cognitive load mitigation techniques. The findings demonstrate significant practical implications for educators, developers and policymakers aiming to advances the inclusive educational technology through a shift from standardized digital tools toward neurodiverse centric design. Thus, it moves beyond to create a more responsive literacy environment for dyslexic students.

FROM DISTRIBUTION TO TRANSFORMATION: A CONCEPTUAL PREDICTIVE ANALYTICS FRAMEWORK FOR SUSTAINABLE POVERTY ALLEVIATION THROUGH ZAKAT

Zanariah Idrus — 2026-03-31

Zakat represents one of the most institutionalized methods of Islamic social finance aimed at redistributing wealth and alleviating poverty. Despite increasing zakat collections across Muslim majority countries, evidence of sustainable poverty exit among recipients remains limited, as most zakat governance models emphasize distribution efficiency rather than long term socioeconomic transformation. This paper proposes a conceptual Poverty-to-Prosperity (P2P) Predictive Zakat Governance Framework that integrates predictive analytics with Islamic social finance principles to enhance targeting accuracy and intervention effectiveness. Using a conceptual development approach, the study synthesizes insights from poverty dynamics, capability theory, Maqasid al-Shariah, and data-driven governance to construct a multi-layered framework that classifies zakat recipients based on poverty risk trajectories and aligns assistance with differentiated intervention strategies. The framework introduces a probability-based stratification model supported by dashboard monitoring to strengthen institutional transparency and outcome-oriented decision making. The proposed framework contributes to the emerging literature on digital transformation in Islamic social finance by offering a structured approach for integrating predictive technologies into zakat management systems. Policy implications for data-driven zakat governance and future empirical validation are discussed.