MJoC is indexed in MyCite and DOAJ. The Malaysian Journal of Computing (MJoC) ( e-ISSN: 2600-8238) is a peer-reviewed, open-access international refereed journal. MJoC will be accepting research papers in Computer science. PUBLICATION IS FREE OF CHARGE. MJoC is indexed in MyCite and DOAJ. MJoC is also available at Google Scholar and ADL. CLICK HERE TO SUBMIT MANUSCRIPT
LRU CACHING POLICY BASED ON NAÏVE BAYES ML ALGORITHM IN COLLABORATIVE PEER-TO-PEER SYSTEMS FOR NETWORK BANDWIDTH UTILIZATION
Web caching offers several advantages, such as increasing cache hit rates, lowering the workload on origin servers, and minimizing network traffic. Nevertheless, limited cache capacity poses a major challenge in web caching systems. Moreover, repeatedly fetching same media objects from origin servers leads to unnecessary bandwidth consumption. Furthermore, traditional caching policies, including Least Recently Used (LRU), are vulnerable to cache pollution. This study introduces a collaborative caching policy based on the Naïve Bayes (NB) Machine Learning (ML) algorithm. The proposed policy exploits structured peer-to-peer architectures, allowing cache contents to be shared among peers to improve the efficiency of LRU web caching policy. Performance evaluation is conducted through simulations using two real-world datasets obtained from YemenNet Internet Service Provider (ISP) and the IRCache network. The results show that the proposed policy outperforms the traditional LRU policy in terms of Hit Ratio (HR), Byte Hit Ratio (BHR), and Cost Throughput (CT).
Copyright (c) 2026 WAHEED YASIN https://creativecommons.org/licenses/by-sa/4.0
TEXTUAL ADVERSARIAL EXAMPLE GENERATION USING BIGRAM UNIGRAM-SEMANTIC PRESERVATION OPTIMIZATION ALGORITHM
The vulnerability of Natural Language Processing (NLP) models to adversarial attacks remains a critical challenge in the field of cybersecurity and AI robustness. While deep learning models have achieved high performance in sentiment analysis, they are susceptible to subtle input perturbations that induce misclassification. This study presents the design and practical implementation of a web-based system (Proof of Concept) that automates the generation of textual adversarial examples using the Bigram Unigram-Semantic Preservation Optimization (BU-SPOF) algorithm. Rather than proposing a novel attack algorithm, our primary contribution is the architectural integration of a dual-source candidate generation strategy (WordNet and OpenHowNet) and a Probability Weighted Word Saliency (PWWS) mechanism to perturb input text while maintaining linguistic coherence. The system was evaluated against a Long Short-Term Memory (LSTM) sentiment classifier using the IMDB dataset.
Copyright (c) 2026 Noor Adam Bin Noor Azmi, Ts. Dr. Haslizatul Fairuz Binti Mohamed Hanum https://creativecommons.org/licenses/by-sa/4.0
TEACHER READINESS AND ATTITUDES TOWARD DIGITAL TECHNOLOGY IN MATHEMATICS CLASSROOMS
This study explores how digital technology is integrated into mathematics education, focusing on the views of secondary school mathematics teachers and using a quantitative research method through a survey. Descriptive and inferential statistical analyses were performed to examine the factors influencing technology adoption, identify its barriers, and suggest strategies for enhancement. The novelty of this study lies in its development of an integrated perspective that simultaneously examines teachers’ readiness, experiential background, and attitudes alongside systemic constraints such as training, resource availability, and institutional support within the context of mathematics education. However, challenges such as inadequate training, limited resources, and lack of institutional support impede effective implementation. Therefore, to overcome the issues, the study seeks the perceptions of 108 mathematics teachers in secondary schools using a questionnaire based on a 5-point Likert scale, ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The study contributes to existing literature by offering empirical evidence on the interconnected nature of these factors and by proposing targeted, context-specific strategies to enhance digital integration in mathematics classrooms. Key recommendations include tailored professional development programs, the promotion of collaborative learning environments, and better infrastructure to equip teachers with the necessary skills and resources for smooth technological integration. By addressing the challenges, educators can foster more enriching teaching and learning experiences in mathematics, ultimately improving outcomes in the digital era.
Copyright (c) 2026 Zarith Sofiah Othman, Nor Anis Amierah Mhd Pizor https://creativecommons.org/licenses/by-sa/4.0
THE IMPACT OF MACROECONOMIC VARIABLES ON PROPERTY PRICES IN MALAYSIA DURING THE COVID-19 PANDEMIC
The housing market is a critical driver of economic growth and a source of financial stability. In Malaysia, residential property prices are influenced by a range of macroeconomic factors, including Gross Domestic Product (GDP), exchange rates, unemployment rates, and wage levels. The COVID-19 pandemic introduced significant disruption and volatility to the Malaysian economy, making it essential to understand the relationships between these factors and property prices. The pandemic's economic challenges, coupled with rising property prices, have exacerbated housing affordability issues. While previous studies focused on long-term trends (1991–2019), there is a gap in understanding the high-frequency (monthly) volatility specifically during the COVID-19 shock. As such, this study aims to investigate the short- and long-term effects of GDP, exchange rates, unemployment, and wages on Malaysian residential property prices from January 2020 to March 2023 (a period of 39 months). Using the Autoregressive Distributed Lag (ARDL) model, monthly data are analyzed to examine how these variables have influenced the housing market. The results indicate that in the long term, GDP growth has no significant effect on property prices, while exchange rates and wages show significant negative impacts. Short-term findings reveal a positive influence of unemployment, potentially reflecting government interventions during the crisis. A robust adjustment mechanism toward equilibrium is confirmed by the error correction term. The study concludes that macroeconomic variables play a significant role in determining property prices during periods of crisis, with exchange rate depreciation and wage disparities intensifying affordability challenges. The positive short-term impact of unemployment likely stems from government stimulus and loan moratoriums, which temporarily buoyed the market.
Copyright (c) 2026 NURUL SIMA, Nurin Nayli, Aldo Eko Syaputra https://creativecommons.org/licenses/by-sa/4.0
UNCOVERING ETHICAL VALUES IN INFORMATION SYSTEMS DEVELOPMENT METHODOLOGIES: A SYSTEMATIC LITERATURE REVIEW
Information system development involves a lot of tools, infrastructures, and humans. The many phases in system development need humans to interact and work with each other to obtain the goal: to complete information system development. These interactions of humans require the right ethics to ensure that the information system development can be successful. For example, ethical leadership is a vital component in determining the success of any project related to information system development in organizations. The aim of this study is to identify the ethical values which are important in information system development methodology phases. This systematic literature review (SLR) comprises of four databases, and these are IEEE Xplore, ProQuest Dissertations and Theses, Scopus, and Emerald Insight, using the PICOC framework and applying several inclusion and exclusion criteria. 172 papers from 2018 to present were found to have discussed the ethical values that are needed in the system development methodology phases. The study finds that there are 10 ethical values highlighted in those papers, for example: ethical leadership, ethical team dynamics, trust, consideration, and so on. The findings from this paper are important for the people who are involved in system development methodology to practice the right ethical values to ensure the success of the system development.
Copyright (c) 2026 Zahidah Zulkifli, Noor Hayani binti Abd Rahim, Tahmid Alam, Fakhrul Hazman Yusoff, Mohammad Masudur Rahman https://creativecommons.org/licenses/by-sa/4.0
INFLUENCE OF CORIOLIS FORCE ON CONTROLLED DOUBLE-DIFFUSIVE MARANGONI CONVECTION IN NANOFLUID LAYERS
Controlled double-diffusive Marangoni convection in a rotating nanofluid layer, heated from below, is studied. Various types of lower-upper boundary conditions, including free-free and rigid-free, are considered. The nanofluids model incorporates the mechanisms of Brownian motion and thermophoresis in nanofluids. The stability of the nanofluids model is analysed using a linear stability analysis based on the normal mode technique. The eigenvalue problem is numerically solved using the Galerkin technique and computational simulations are carried out using Maple software. The influences of several parameters are examined and presented graphically, including modified diffusivity ratio, nanoparticles concentration, solutal Rayleigh number and Soret effects. These effects are found to contribute to the advancement of Marangoni convection, a phenomenon that occurs due to variations in surface tension along the interface of a nanofluid layer. Conversely, the presence of the Coriolis force (due to the rotation), controller gain, and Dufour effects are observed to slow down the process of Marangoni convection.
Copyright (c) 2026 IZZATI KHALIDAH KHALID, Ahmad Syukri Mohd Shukor, Mohd Rijal Ilias, Nurul Hafizah Zainal Abidin, Bekzodjon Fayziev https://creativecommons.org/licenses/by-sa/4.0
A CONCEPTUAL FRAMEWORK FOR ROBUST FEW-SHOT LEARNING: INTEGRATING UNBALANCED OPTIMAL TRANSPORT AND SELF-SUPERVISED TRANSFORMER REPRESENTATIONS
Few-shot learning (FSL) aims to enable deep models to generalise from extremely limited labelled data, yet unstable metric matching, distribution imbalances, and weak structural representations in low-data regimes often constrain its performance. This paper proposes a conceptual framework that unifies metric-based similarity learning, Unbalanced Optimal Transport (UOT) via Unbalanced Sinkhorn Distance (USD), and self-supervised Transformer representations to conceptually address the theoretical and structural limitations of existing FSL approaches. The framework theoretically unifies distribution-aware USD matching, SSL-enhanced ViT/Swin feature representations, and metric-based inference within a coherent pipeline. This work aims to provide a theoretical foundation and research roadmap for future empirical studies on robust few-shot learning under realistic, distributionally complex conditions.
Copyright (c) 2026 HAYATI ABD RAHMAN, Pang Yun https://creativecommons.org/licenses/by-sa/4.0
HYBRID PORTFOLIO OPTIMIZATION WITH PCA, CLUSTERING, AND THE BARZILAI-BORWEIN METHOD
Portfolio optimization aims to balance risk and return by identifying an effective mix of assets. In this study, we integrate principal component analysis (PCA) and hierarchical clustering for stock selection with the Barzilai–Borwein (BB) gradient method for portfolio optimization. Forty-eight U.S. stocks from the Kaggle fundamental stock dataset were initially collected, and 42 stocks were retained after preprocessing. Financial ratios from 2006 and adjusted closing prices from 2016–2017 were analysed, with one representative stock from each cluster selected using the Sharpe ratio. The BB method was then applied to determine optimal weights, ensuring full capital allocation without short selling. Among the tested approaches, the Barzilai–Borwein gradient method 1 (BB1) step size achieved strong performance, producing an annual return of 25.6% while maintaining relatively low volatility. The portfolio also generated a Jensen’s alpha of 1.55, confirming the presence of positive abnormal returns beyond market expectations. These results suggest that combining PCA-based clustering with the BB optimization method offers a practical and efficient way to construct diversified portfolios. The study highlights the BB algorithm’s potential as a lightweight yet effective alternative to more complex optimization techniques in financial decision-making.
Copyright (c) 2026 Wei Yeing Pan, Kai Shin Chiew, Hong Seng Sim, Jia Hou Chin, Yap Jia Lee https://creativecommons.org/licenses/by-sa/4.0
DEVELOPMENT OF NEIGHBOURHOOD COMMUNITY-BASED SYSTEM USING ACTIVITY THEORY METHOD FOR REQUIREMENT IDENTIFICATION AND ANALYSIS
Community engagement and efficient information dissemination are essential for well-functioning neighbourhoods. This project focused on the development of a community-based system in Taman Lapangan Jaya using Activity Theory to guide requirement identification and analysis. The current communication method, which relies on group chats managed by the neighbourhood president, has led to fragmented information, difficulty in accessing documents such as committee contact lists and annual reports, and frequent miscommunication. To address these issues, this study aimed to design and implement a centralized system that meets the residents’ needs. A prototyping approach was employed, beginning with planning and requirement elicitation through stakeholder interviews and the application of Activity Theory. The iterative development process involved quick design, prototype creation, user feedback, and refinement. The system design incorporated class diagrams, a data dictionary, and database structures, leading to a fully developed and operational platform. The final system provides a user-friendly, structured solution for information sharing and communication within the community. This study contributes to the field of requirement engineering by demonstrating the application of Activity Theory in the development of a neighbourhood-focused system and offers a model that can be replicated in similar community settings.
Copyright (c) 2025 KAMALIA AZMA KAMARUDDIN, Norsyazwani Hafizah Reduan https://creativecommons.org/licenses/by-sa/4.0
TOWARDS A UNIFIED FRAMEWORK FOR KNOWLEDGE TRACING WITH GRAPH CONVOLUTIONAL AND NEURAL ARCHITECTURES
This paper sets out to propose a unified theoretical framework for knowledge tracing (KT) that combines graph convolutional networks (GCNs) with neural sequence architectures in intelligent tutoring systems. While existing methods have achieved some success, they face limitations in modelling relational dependencies among concepts and the temporal progression of learner behaviour. Building on socio-constructivist views of knowledge as a network of relations and connectionist accounts of learning as adaptation over time, the framework integrates graph-based relational reasoning with sequence-based temporal modelling. The argument advanced here is that the integration offers interpretable representations of knowledge states while preserving predictive performance. The paper draws together recent developments in graph-enhanced KT and attention-based models and outlines design heuristics for scalable deployment. Key issues are identified, including computational cost, data sparsity, and explainability for classroom use. It is anticipated that the framework will inform the design of more systems and provide a tractable agenda for empirical validation across multiple domains and learner populations.
Copyright (c) 2025 Yaxi Su, Mohamad Yusof Darus, Norizan Mat Diah, Jinpeng Huang, Azlin Ramli https://creativecommons.org/licenses/by-sa/4.0
Utilizing Mathematical Software for the Teaching and Learning of Mathematics in Malaysia
The paper examines the effects of mathematical software on the academic performance of secondary school students, along with its roles in facilitating technological adaptation among the students in mathematics education. Supported by the growing trend of technology use in schools that has led to the intensified use of mathematical software among teachers, the study was carried out in Sekolah Menengah Kebangsaan Meru, Klang, with a sample size of 100 student respondents. This research aims to achieve two primary goals: first, to evaluate how effective the use of mathematical software is in improving secondary school students' comprehension of mathematics; and secondly, to measure the degree of technology integration among students in the field of mathematics education. The research was premised on a quantitative design using data gathered from questionnaires administered via Google Forms, after securing the necessary approvals. The results point out the implications and the importance of mathematics software, particularly its potential to promote a better understanding of mathematics, enhance flexibility and critical thinking, improve learning interest in class, and provide an interactive learning process. In general, this study points out the beneficial impact of mathematics software on secondary school students' academic performance and their willingness to use technology in learning mathematics. It is suggested that educators integrate these technological aids into their pedagogical practices to enhance the potential and benefits inherent in mathematics learning.
Copyright (c) 2025 Naiim Fatimi, Saufianim Jana Aksah, Dr. Izni, Dr. Huda, Dr. Mohammed https://creativecommons.org/licenses/by-sa/4.0
MODIFIED HUNGARIAN METHOD FOR LECTURER-TO-COURSE ASSIGNMENT: A MULTI-OBJECTIVE MATHEMATICAL PROGRAMMING MODEL FOR OPTIMIZING PREFERENCES AND COMPETENCY (PC MO-MHM)
Efficient lecturer-to-course assignment is crucial for ensuring both faculty satisfaction and optimal teaching outcomes in higher education institutions. This study presents an advanced optimization model based on the Modified Hungarian Method (MHM) to address this challenge by integrating lecturers' preference levels and competency scores. While previous research has primarily focused on the traditional Hungarian Method (HM), limited attention has been given to its modified version. Moreover, the incorporation of preference-competency-based criteria in lecturer assignments is still lacking. To bridge these gaps, this study develops a mathematical programming approach to refine the MHM framework. The proposed model, called the Preference-Competency Multi-Objective MHM (PC MO-MHM), aims to achieve two key objectives: maximizing lecturers’ preferences and maximizing lecturers’ competencies. Competency is assessed across three elements: knowledge, skills, and teaching motivation. Data were gathered through an online survey involving Mathematics lecturers teaching undergraduate courses at the public university in Malaysia. By utilizing the collected data on preference levels and competency scores, the PC MO-MHM model was implemented using MATLAB’s intlinprog function to generate an optimized lecturer-to-course assignment plan, limiting each lecturer to a maximum of three courses. The findings highlight that the PC MO-MHM model effectively determines the most suitable course assignments based on lecturers’ preferences and competencies. The enhanced MHM framework provides a practical tool for optimizing course-teaching assignment planning. The model potentially not only improves teaching quality but also minimizes mismatches between lecturers and courses, fostering better academic outcomes and increased faculty satisfaction. Ultimately, this study contributes towards refining lecturers’ assignment processes, paving the way for more effective and efficient resource management in academia.
Copyright (c) 2025 Nur Syahirah Ibrahim, Adibah Shuib, Zati Aqmar Zaharudin https://creativecommons.org/licenses/by-sa/4.0
A MULTICRITERIA DECISION MODEL FOR SUSTAINABLE PORT FUNCTIONS IN ENVIRONMENTAL PERSPECTIVES
Port operations, while critical to global trade and economic growth, contribute significantly to environmental pressures through activities such as vessel traffic, cargo handling, dredging, and on-dock transportation, leading to emissions of greenhouse gases and other pollutants. Hence, it is of priority to have a systematic evaluation and prioritization of sustainability measures across port functions. This study employs a multi-criteria decision-making (MCDM) approach, the Analytic Hierarchy Process (AHP), to evaluate and prioritize key functional roles and subfunctions that contribute to port sustainability performance. By engaging expert judgment from two leading ports in Malaysia, the analysis identifies stakeholder priorities on the emphasis among landlord, operational, regulatory and community functions in sustainable port management. The structured AHP framework ensures transparency and reproducibility of the weighting process while remaining adaptable to incorporate emerging sustainability measures, such as green hydrogen bunkering. The results show that the experts from two major ports expressed differing perspectives on the highest priority function with the Landlord function (at 0.370) being the top priority for Northport and the Regulatory Function (at 0.664) to be ranked highest for Johor Port. Details analysis indicates that the difference is not significant. The findings may offer useful insights for guiding resource allocation, encouraging consensus-building workshops to align stakeholder perspectives, and providing a preliminary foundation for future research on sustainability frameworks. Moreover, this study can contribute to supporting policymakers, port practitioners, and researchers in making more informed and balanced decisions toward advancing environmentally sustainable port operations.
Copyright (c) 2025 Muhamad Safuan Shamshol Bahri, S. Sarifah Radiah Shariff, Nazry Yahya, Chang Won Lee https://creativecommons.org/licenses/by-sa/4.0
E-COMMERCE REQUIREMENT ANALYSIS AND DESIGN WITH FOGG BEHAVIOR MODEL (FBM)
This paper explores how the Fogg Behavior Model (FBM) can be used to design a more effective e-commerce platform. FBM focuses on three key factors that influence user behavior: motivation, ability, and triggers. Using Carro Empire, a small car accessories business in Malaysia, as a case study, the research identifies user needs through interviews and maps them to system features like 3D product views, easy navigation, and timely reminders. A working prototype was developed and reviewed by the business owner, showing that FBM helps create a more user-friendly and persuasive online shopping experience. For future work, the study recommends conducting user testing, such as surveys or A/B testing, to measure how FBM-based features impact customer behavior and improve the system further.
Copyright (c) 2025 Fauziah Ahmad, Norjansalika Janom, Syafiyana Elysha Asmadi https://creativecommons.org/licenses/by-sa/4.0
HUMAN-COMPUTER INTERACTION EXPERIENCE AND CUSTOMER LOYALTY IN CHINESE HOTELS: THE MODERATING EFFECT OF BRAND TRUST
In the era of digital transformation, human-computer interaction has become a critical factor in shaping customer experiences and loyalty, particularly in the hospitality industry. This study investigates the relationship between human-computer interaction experiences and customer loyalty in Chinese hotels, with a focus on the moderating role of brand trust. Drawing on the Technology Acceptance Model, the research explores how perceived ease of use and perceived usefulness of human-computer interaction systems influence customer loyalty while considering the impact of brand trust as a moderating variable. Using a quantitative research design, data were collected from 350 hotel guests in China who had interacted with human-computer interaction systems such as mobile apps, chatbots, and self-service kiosks. The findings reveal that human-computer interaction experience significantly enhances customer loyalty, and this relationship is positively moderated by brand trust. Specifically, customers with higher levels of brand trust are more likely to remain loyal despite potential technical issues or usability challenges. The study underscores the importance of user-centric human-computer interaction design, trust-building initiatives, and cultural sensitivity in enhancing customer loyalty in the Chinese hospitality market. These insights offer valuable implications for hotel managers and technology developers aiming to optimize human-computer interaction systems to foster long-term customer relationships.
Copyright (c) 2025 Lu Xie, Yuan Xinying, Chen ZeYu, Johanudin Lahap https://creativecommons.org/licenses/by-sa/4.0
EXAMINING THE RELATIONSHIP BETWEEN IT PROJECT RISK MANAGEMENT AND BENEFITS REALIZED FROM IT PROJECT IMPLEMENTATION WITHIN THE PUBLIC SECTOR
Information technology (IT) has become increasingly critical in today's digital world, supporting, sustaining, and driving growth in public organizations. The board of directors and senior management have traditionally delegated, disregarded, or avoided IT decisions. The majority of public sector organizations have realized that such a mindset is outdated and must be avoided. Being a foundation for effective enterprise governance, the IT risk management domain seeks to guarantee that a structure exists for identifying, analyzing, mitigating, monitoring and communicating an organization's IT risk to ensure successful IT project implementation. This research, therefore, sought to examine the relationship between risk management of IT projects and the hypothesized benefits from IT investments, namely Return on Investments (ROI), number of customers served and the level of customer service delivery. A total of 309 out of 517 public sector organizations in Kenya were sampled, and data were collected from heads of departments, including IT, Finance, HR, Audit, and Technical among others, through an online research tool. The correlation results revealed that IT risk management and ROI had a weak and negative correlation. The correlation between the management of IT risk and the number of customers served was equally found to be weak and negative. However, the correlation between IT risk management and customer service delivery was positive and significant. Results from the multivariate regression test revealed that risk management of IT projects significantly improved customer service delivery but had no significant effect on both ROI and number of customers. The study concluded that there was a significant effect of IT risk management on the customer service delivery. Therefore, senior management had a responsibility to ensure that IT risk management was incorporated in all major IT projects to ensure that improved customer service delivery benefit was realized.
Copyright (c) 2026 Fredrick Bitta, Prof. Robert Oboko, Prof. Christopher Chepken https://creativecommons.org/licenses/by-sa/4.0
ADDRESSING CLASS AND DEMOGRAPHIC IMBALANCE IN E-COMMERCE BEHAVIOR PREDICTION: A CASE STUDY USING SMOTE AND RESAMPLING TECHNIQUES
In e-commerce predictive modeling, imbalanced data is a common challenge especially when both class and demographic attributes are unequally distributed. This study explores the combination of Synthetic Minority Oversampling Technique (SMOTE) and demographic resampling to improve the performance of models predicting online purchasing behavior in Malaysia. The first step is to use SMOTE to handle class imbalance in the five-point purchase intention scale classification. The second step is followed by gender imbalance, which is caused by a higher number of female respondents than male, this problem is solved through a combination of down-sampling and up-sampling. Further adjustments were made to balance other demographic factors such as age, employment status, and ethnicity. For this study the dataset, encompassing 1,126 survey responses, was analyzed using WEKA tools with six classifiers: J48, Random Tree, REPTree, JRip, PART, and OneR. Each classifier was evaluated in three stages: original data, after SMOTE, and after both SMOTE and demographic balancing. The results displayed clear improvements in model performance. For example, J48’s accuracy increases from 62.85% (unbalanced) to 98.69% (fully balanced), while Random Tree reached 99.29%. These results highlight the importance of addressing both class and demographic imbalances to develop better and more reliable models. The study improves to the limited research that combines SMOTE with demographic resampling in Malaysian e-commerce and offers practical insights for building better predictive models to support customer segmentation, targeting, and personalization. Future work could explore balancing more attributes and applying the method to ensemble or deep learning models for improved performance and interpretability.
Copyright (c) 2026 NURUL AIN BINTI MUSTAKIM, Maslina Abdul Aziz, Shuzlina Abdul-Rahman, Rahmiati Rahmiati https://creativecommons.org/licenses/by-sa/4.0
PROCESS SIMULATOR FOR MANUAL HANDLING PESTICIDE PACKAGING ASSEMBLY LINE
Reducing production costs without sacrificing product quality is essential for the global market survival. The problem of line balancing, especially assembly line balancing, plays an important role for industry in achieving the highest quality and lowest cost. This project presents the simulation of line balancing the pesticide packaging assembly line in pesticidebased company. This study aims to develop a productive and effective way of assembling the product to meet customer demands. Line balancing is about arranging a production line so that the production flow from one workstation to the next is even. Line balancing also provides a successful tool to reduce bottleneck by balancing the task time of each workstation so that no delays occur, and nobody is overburdened with their work. The whole project contains study of line balancing through theoretical calculation and analysis done in the simulation model. The simulation was performed using the Process Simulator. Process simulator has been used to simulate the layout condition based in precedence diagram. The first step in using Process Simulator is to create a simulation model of a process in the Visio layout. This is accomplished by adding activities, entities, and resources to the Visio layout and then connecting them with arrival and routing connections. Overall simulation result shows that total unit produce for 5 weeks is 12,240 cartons with average time in system 11,293.85 minutes and average time in operation is 2.39 mins. Filling process and manual labelling process shows highest operation percentage. The percentage of blocked items increased before the bottleneck process. The assembly line plays a critical role in enabling the factory to deliver the right quantity and quality on time.
Copyright (c) 2024 Siti Norhazlinda Roslan, Bulan Abdullah, Nor Hayati Saad, Razali Hassan, Siti Khadijah Alias https://creativecommons.org/licenses/by-sa/4.0
MODELING AND PREDICTING THE DYNAMICS OF COVID-19 IN MALAYSIA: A STATE-SPACE APPROACH
e emergence of COVID-19 in Malaysia in January 2020 marked the beginning of a significant public health challenge. Despite the transition to the endemic phase on April 1, 2022, the global impact of the virus remains substantial. This research aims to forecast the cumulative number of detected cases and deaths by employing a state-space model derived from the Susceptible-Infectious-Recovered (SIR) model, capturing the multi-wave dynamics of COVID-19. The modeling focuses on estimating the trends within the time interval spanning from week 1 to week 12, commencing in mid-June 2022. Real-time data sourced from the Ministry of Health in Malaysia serve as the basis for model development and validation, utilizing MATLAB and Simulink for simulation purposes. The findings of the simulation reveal a direct correlation between the number of detected cases and deaths, suggesting a positive relationship with the real-life situation. This mathematical representation contributes to a deeper understanding of the ongoing dynamics of COVID-19 and provides a tool for predicting future trends, aiding in public health planning and response efforts.
Copyright (c) 2024 Wan Munirah Wan Mohamad, Syazwani Mohd Salleh, Tengku Farah Busyra Tengku Nadzion, Abdul Latif Mohd Riza, Azmirul Ashaari https://creativecommons.org/licenses/by-sa/4.0
PSYCARIA - EMOTION DETECTOR FOR A PSYCHIATRIST
Every person will experience stress around the world, some healthy, called EUSTRESS and some unpleasant, named DISTRESS. Good pressure and stress promote success. Stress damages people's lives and health and causes various diseases. On the other hand, psychiatrists have a hard time treating their patients owing to a lack of time. They need innovative and intelligent equipment to treat their patients. We prepared a device that can detect a person's POSITIVE and NEGATIVE emotions through a smartwatch and a gadget that can sense body temperature, respiration, and heart rate. After witnessing these parameters, it can store the results on a website depending on the patient's condition. For example, the psychiatrist observed one patient for at least seven days regarding the days' results stored on a website. After seven days, the report is generated. The goal of psychiatrists in keeping their patients for seven days is to assess their emotional health and determine if they need to adjust their treatment. This system detects eight positive and negative emotions through heartbeat, respiratory, and body temperature sensors. These sensors are incorporated by utilizing machine learning. Web-based apps interpret sensor readings. Psychiatrists will analyze and report the website's results.
Copyright (c) 2022 Ayesha Anees Zaveri, Mobeen Nazar, Nabiha Faisal, Sarama Shehmir, Misbah Parveen, Naveera Sami https://creativecommons.org/licenses/by-sa/4.0
VULGARIZED NEIGHBOURING NETWORK OF MULTIVARIATE AUTOREGRESSIVE PROCESSES WITH GAUSSIAN AND STUDENT-T DISTRIBUTED RANDOM NOISES
This paper introduces the vulgarized network autoregressive process with Gaussian and Student-t random noises. The processes relate the time-varying series of a given variable to the immediate past of the same phenomenon with the inclusion of its neighboring variables and networking structure. The generalized network autoregressive process would be fully spelt-out to contain the aforementioned random noises with their embedded parameters (the autoregressive coefficients, networking nodes, and neighboring nodes) and subjected to monthly prices of ten (10) edible cereals. Global-α of Generalized Network Autoregressive (GNAR) of order lag two, the neighbor at the time lags two and the neighbourhood nodal of zero, that is GNAR (2, [2,0]) was the ideal generalization for both Gaussian and student-t random noises for the prices of cereals, a model with two autoregressive parameters and network regression parameters on the first two neighbor sets at time lag one. GNAR model with student-t random noise produced the smallest BIC of -39.2298 compared to a BIC of -18.1683 by GNAR by Gaussian. The residual error via Gaussian was 0.9900 compared to the one of 0.9000 by student-t. Additionally, GNAR MSE for error of forecasting via student-t was 15.105% less than that of the Gaussian. Similarly, student-t-GNAR MSE for VAR was 1.59% less than that of the Gaussian-GNAR MSE for VAR. Comparing the fitted histogram plots of both the student-t and Gaussian processes, the two histograms produced a symmetric residual estimate for the fitted GNAR model via student-t and Gaussian processes respectively, but the residuals via the student-t were more evenly symmetric than those of the Gaussian. In a contribution to the network autoregressive process, the GNAR process with Student-t random noise generalization should always be favoured over Gaussian random noise because of its ability to absolve contaminations, spread, and ability to contain time-varying network measurements
Gaussian Generalized Network Autoregressive (GNAR)
Copyright (c) 2023 Rasaki Olawale Olanrewaju, Ravi Prakash Ranjan, Queensley C. Chukwudum, Sodiq Adejare Olanrewaju https://creativecommons.org/licenses/by-sa/4.0
DOES GOOGLE TRANSLATE AFFECT LEXICON-BASED SENTIMENT ANALYSIS OF MALAY SOCIAL MEDIA TEXT?
There are a lot of sentiment resources for English, however, there are limited resources in a resource-poor language like the Malay language. One approach to improving sentiment analysis is to translate the focus-language text to a resource-rich language such as English by using Machine Translation (MT). However, when text is translated from one language into another, sentiment is preserved to varying degrees. The objective of this paper is to assess the performance of MT in Google Translate towards sentiment analysis of Malay social media text on Facebook pages of a caregiver of a person with autism. A total of 3,525 Facebook comments in the Malay language were gathered from May to October 2020. The comments were manually translated to English to create dataset_manual. Google Translate was used to automatically translate the Malay comments into English creating dataset_auto. The sentiment polarity of each comment was labeled as a ground truth dataset. A lexicon-based approach was used to extract sentiment from both dataset_manual and dataset_auto to determine the sentiment polarity. Results show that 65.9% of sentiment analysis using dataset_auto significantly reduces sentiment analysis. The sentiment expressions are often mistranslated into neutral expressions when translated. Meanwhile, sentiment analysis using dataset_manual was still able to capture the sentiment of Facebook comment without taking the comment out of context where 92.5% shows positive sentiment towards comments related to autism spectrum disorder.
Copyright (c) 2022 Vanessa Enjop, Rosanita Adnan, Nursuriati Jamil, Sanizah Ahmad, Zarina Zainol, Siti Arpah Ahmad https://creativecommons.org/licenses/by-sa/4.0
THE RELATIONSHIP BETWEEN SOCIOECONOMIC FACTORS WITH FERTILITY RATE IN MALAYSIA
The issue of an ageing population is on the rise globally, and Malaysia is not excluded from facing this issue. Past literature has recognized that the declining fertility rate is one of the main factors that lead to this problem. However, along with the declining fertility rate in Malaysia over time, there are also changes in social and economic factors such as increment in female labor participation, higher house income and consumption expenditure, increasing inflation and changes in the demographic of the population. The study has become a concern since these factors could affect the declining fertility rate. Hence, this study is conducted (1) to analyze the significant socioeconomic factors that affected fertility rate decline in Malaysia; (2) to investigate the short-run and long-run relationship between socioeconomic factors and fertility rates in Malaysia; and (3) to determine the direction of the causal relationship between each of the socioeconomic factors and fertility rates in Malaysia. The Autoregressive Distributed Lag (ARDL) model was conducted for the first and second objectives, while the Granger Causality test was conducted for the third objective. The research is conducted to investigate the stated relationship between the period in the year 1982 to 2019. It can be concluded that income, household consumption expenditure and the female labor force are related to fertility in the short and long run. At the same time, inflation and population ethnicity are only related in the long run.
Female Labor Participation and Household Income
Copyright (c) 2022 Ahmad Muadz Zulqarnain, Mazlynda Md Yusuf https://creativecommons.org/licenses/by-sa/4.0
BIG DATA ANALYTICS FOR SUMMIT GROUP HOSPITAL USING ENTERPRISE ARCHITECTURE AS STRATEGIC APPROACH
Healthcare is known for being a highly intensive engagement with a complex organisation, and it involves numerous levels of stakeholders. A big data analytics solution is required to simplify and improve the process’s overall data efficacy and flow. However, there are many challenges in implementing big data analytics in a healthcare organisation, as evidenced in some situations. The difficulties that must be addressed are high costs, time-consuming processes in establishing strategic management, and becoming a patient-centred organisation with optimal coordination. As a result, certain studies that have been conducted are suggested a feasible approach for big data analytics is by implementing Enterprise Architecture (EA) in health organisations. The TOGAF ADM model has been chosen as the methodology for implementing EA in a healthcare organisation due to the power of its flexible methods in merging artefacts and its focus on processes. When adopting EA, four architecture layers are examined: Business, Data, Application, and Technology (BDAT). The problems and As-Is environment have been explored, implying that healthcare organisations require EA to assure continuous service delivery. Significantly, the proposed approach will aid stakeholders in quickly adopting the business transformation through the use of EA
Copyright (c) 2022 Kholijah Norbib, Nur Nesa Nashuha Ismail, Nur Azaliah Abu Bakar, Surya Sumarni Hussein https://creativecommons.org/licenses/by-sa/4.0
HAMMING DISTANCE OF GENERALISED L-R INTUITIONISTIC FUZZY NUMBER AND ITS APPLICATION
This paper proposes a distance measure for generalised L-R intuitionistic fuzzy number (GLRIFN) which is Hamming distance, aiming to enhance the theoretical and practical tools available for decision-making under uncertainty. The properties of the Hamming distance of generalised L-R intuitionistic fuzzy number are also discussed in this study. GLRIFN extends traditional L-R intuitionistic fuzzy number by incorporating confidence level for both membership and non-membership functions, making them more reliable in the evaluation process. To demonstrate the practical utility of the proposed measure, it is applied within the Generalised L-R Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (GLRIF-TOPSIS), a multi-criteria decision-making (MCDM) method. A real-world case study on river water pollution classification is conducted, wherein the proposed model effectively evaluates the pollution levels of different rivers by capturing the nuances of imprecise, vague, and conflicting environmental data. The results show that the River is the cleanest river, while the River is the most polluted river. The integration of the Hamming distance with GLRIF-TOPSIS offers a structured and adaptable decision-making framework, capable of addressing complex multi-criteria problems across domains characterised by high levels of ambiguity. This contribution not only enriches the existing body of fuzzy set theory but also opens avenues for further applications in environmental assessment and other areas that require robust fuzzy modeling.
Copyright (c) 2025 MUHAMMAD ASYRAN SHAFIE, Nor Hanimah Kamis, Daud Mohamad, Seripah a https://creativecommons.org/licenses/by-sa/4.0
HEURISTIC EVALUATION OF E-COMMERCE MARKETPLACE
This paper adapts one of the most popular heuristic evaluation techniques to effectively assess the design of an e-commerce marketplace. Heuristic evaluation for the e-commerce marketplace with a detailed explanation is presented as a guideline for the e-commerce marketplace. This research involved usability evaluation that analyses the e-commerce marketplace using a set of design characteristics (usability heuristics). The process of discovering usability problems is one of the three objectives of this project. The selected set of heuristics could significantly influence the results (usability findings) of performed heuristic evaluations. Based on the evaluators' evaluation, a set of heuristics was produced. This will be used as a guideline to be applied to the design of the e-commerce marketplace called University Textbook Marketplace (UniText). The result of this research presents the heuristic evaluation for e-commerce marketplace guidelines to perform a cost-effective usability evaluation of e-commerce marketplace applications. Therefore, improving website design that is both pleasurable and meets the user's and customer's needs
Copyright (c) 2022 Maslina Abdul Aziz, Muhammad Hafizul Sani, Hanif Baharin, Suzana Zambri, Nur Idlan Jiman, Ida Normaya Mohd Nasir https://creativecommons.org/licenses/by-sa/4.0
COMPARATIVE ANALYSIS OF LOGICAL ACQUISITION USING WONDERSHARE DR. FONE, MOBILEDIT FORENSIC, AND FONEPAW ON ANDROID PHONES
The increasing number of mobile phone usage in storing confidential information, such as mobile online transactions has led to the growing number of research in mobile forensic. There are lot of crimes may happen through mobile phones, such as smishing, bluejacking, and malware attacks. Mobile forensic is a way of collecting any digital evidence on the mobile phone. Logical acquisition is one of the methods for mobile forensic. The methodology for logical acquisition consists of identification, isolation, acquisition, analysis, and documentation. This research aims to study, perform and analyze logical acquisition using Wondershare Dr. Fone for Android, MOBILedit Forensic, and FonePaw for Android in Android-based phones which are Oppo F9 and Samsung S7 Edge. The analysis is based on percentage of capability and time taken for each tested mobile forensic tool on the two selected Android phones. The results show that FonePaw for Android gets the highest average capability (97%) with the shortest time while MOBILedit Forensic obtains the lowest average capability (74%) with the longest time taken. This research may help the forensic investigators team to determine the suitability of mobile forensic tools with Android phones based on the Android features and type of files
Copyright (c) 2022 Nuraimi Farhana Salimi, Nor Bakiah Abd Warif, Nor-Syahidatul N Ismail https://creativecommons.org/licenses/by-sa/4.0
PREDICTION OF LIFE EXPECTANCY FOR ASIAN POPULATION USING MACHINE LEARNING ALGORITHMS
Predicting life expectancy has become more important nowadays as life has become more vulnerable due to many factors, including social, economic, environmental, education, lifestyle, and health condition. A lot of studies on life expectancy have been carried out. However, studies focusing on the Asian population are limited. This study presents machine learning algorithms for life expectancy based on the Asian population dataset. Comparisons are made between tree classifier models, namely, J48, Random Tree, and Random Forest. Cross validations with 10 and 20 folds are used. Results show that the highest accuracy is obtained with Random Forest with 84% accuracy with 10-fold cross-validation. This study further identifies the most significant factors that influence life expectancy prediction, which includes socioeconomic factors and educational status, health conditions and infectious disease.
Copyright (c) 2022 Nurul Shahira Pisal, Shuzlina Abdul-Rahman, Mastura Hanafiah, Saidatul Izyanie Kamarudin https://creativecommons.org/licenses/by-sa/4.0
PREVALENCE OF RISK FACTORS AND SEVERITY LEVEL OF DIABETIC RETINOPATHY IN UITM MEDICAL SPECIALIST CENTRE:AN ORDINAL ANALYSIS
Diabetic Retinopathy (DR) has become a major concern as the number of diabetic patients who are diagnosed with DR has been on the rise each year. Hence, the adoption of a strategic approach is needed to determine the factors that contribute to the level of severity in DR patients. This research aims to determine the significant risk factors influencing the severity level of DR as well as to evaluate the classification rate between the severity level of 157 patients at UiTM Private Specialist Centre by employing the Ordinal Logistic Regression (OLR) analysis. The study revealed that there were 60 (38%) patients with Mild Non-Proliferative Diabetic Retinopathy (NPDR), 61 (39%) patients with moderate NPDR, 9 (6%) patients with severe NPDR and 27 (17%) patients with Proliferative Diabetic Retinopathy (PDR). On top of that, the severity level of DR was found to be influenced by the duration of diabetes melitus (DM) (p=0.005), nephropathy (p=0.011) and dyslipidemia (p=0.035). Patients who did not have nephropathy were 0.6 times less likely and patients who did not have the dyslipidemia were 0.7 times more likely to have the highest severity level of DR compared to lower severity levels. For the duration of DM, an increase in the duration was associated with an increase in the odds of having the highest severity level. Finally, the results showed that severity level 2 (Mild NPDR), severity level 3 (Moderate NPDR) and severity level 4 (Severe NPDR) were the most accurate categories predicted by the model. This study can contribute to the improvement of health among DR patients and provides alternatives to the hospital in giving treatments to the patients.
Copyright (c) 2025 Aniza Ayuna Che Jani, Zuraida Khairudin, Nornadiah Mohd Razali, Nor Azimah Abd Aziz, Hezlin Aryani Abd Rahman, Nur Syamimi Haji Abu Bakar https://creativecommons.org/licenses/by-sa/4.0
APPLICATION OF LAMPORT DIGITAL SIGNATURE SCHEME INTO THE STATION-TO-STATION PROTOCOL
In cryptography, the key exchange protocol is very important before starting secure communication. Both parties will use an asymmetric key algorithm to exchange their keys for encryption and decryption. One of the methods to securely exchange the keys is Diffie-Hellman Key Exchange (DHKE) protocol. DHKE allows both parties to exchange their keys over the insecure public channel safely. However, DHKE protocol does not authenticate the message, making it easily exposed to third-party interruptions like Man-in-the-Middle (MitM) attack. Therefore, Station to Station (STS) protocols was introduced after modifying the DHKE protocol and adding authentication elements. Thus, this paper proposes to provide the authentication of the STS protocol by using Lamport Digital Signature Scheme. Lamport Digital Signature Scheme, also known as Lamport one-time signature scheme, gives very strong security because it can be built from any cryptographically secure one-way function and usually uses a cryptographic hash function. The results from the study are STS protocol algorithms that provide a verification scheme using the Lamport Digital Signature Scheme. As a result, both parties will obtain a common key for encryption and decryption, in which both parties play a role by using their respective digital signature for verification within the STS of this protocol
Man in the Middle Attack and One-way Function
Copyright (c) 2022 Md Nizam Udin, Farah Azaliney Mohd Amin, Nor Ainaa Mat Abu, Siti Nurfazliana Mohamad Sarif, Intan Nur Athirah Mohammad Zuki https://creativecommons.org/licenses/by-sa/4.0