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
UNIFIED FUNCTIONAL METHOD FOR SOLVING GENERAL POLYNOMIAL EQUATIONS OF DEGREE LESS THAN FIVE
A unified method for solving general polynomial equation of degree less than five that incorporate a computational formula that relate the coefficients of the depressed equation and the coefficient of the standard polynomial equation is proposed in this study. This is to ensure that this method is valid for all degree less than five. It shall apply the undetermined parameter method of auxiliary function to obtain solutions to these polynomial equations of degree less than five in one variable. In particular, the result of our work is a unification and improvement on the work of several authors in the sense that only applicable for the case of polynomial equation of degree one. Finally, our results improve and generalize the result by applying standard formula methods for solving higher degree polynomials. It is recommended that the effort should be made toward providing other variant methods that are simpler and friendly.
Copyright (c) 2021 Dozie Felix Nwosu, Odilichukwu Christian Okoli, Amaka Ezeonyebuchi, Ababu Teklemariam Tiruneh https://creativecommons.org/licenses/by-sa/4.0
PROJECT-BASED INCIDENT RATE BASED ON WEIGHTED AVERAGE METHOD
The incident rate has been widely used to indicate safety performance. The incident rate of a company can be compared with the national or international incident rate within similar industry or among different types of industries. The comparison is particularly very useful as a safety benchmark to gauge performance with other companies in the same business area. However, many existing methods produce the annual incident rate, which has been formulated on an annual basis. This will lead to incompatibility of the method used in calculating the incident rate for a project that runs for a specific period. This is because the annual incident rate does not consider the duration of the project; it becomes less meaningful in indicating the safety performance of project-based activities such as those in construction industries. The proposed method which is Project-Based Incident Rate (PIR) is found to be able to reflect the actual situation of project-based companies better than the existing annual incident rate method, and it is also can be expressed both on a monthly and yearly basis.
Copyright (c) 2021 Khairul Anwar Rasmani, Noreha Mohamed Yusof, Norani Amit, Norliana Mohd Lip, Hazwani Ramli, Nur Nadiah Rosli, Anis Dayana Rusli https://creativecommons.org/licenses/by-sa/4.0
STABILITY ANALYSIS OF A PROPOSED SCHEME OF ORDER FIVE FOR FIRST ORDER ORDINARY DIFFERENTIAL EQUATIONS
This paper presents the stability analysis of a proposed scheme of order five (FCM) for first order Ordinary Differential Equations (ODEs). The proposed FCM is derived by means of an interpolating function of polynomial and exponential forms. The properties of FCM were discussed extensively. The linear stability of FCM in the context of the Third Order One-Step Method (TCM) and Second Order One-Step Method (SCM) for the solution of initial value problems of first order differential equations is presented. The stability region of FCM, TCM and SCM is investigated using the Dahlquist’s test equation. The numerical results obtained via FCM are compared with TCM and SCM. Moreover, by varying the step length, the accuracy and convergence of the methods in terms of the final absolute relative error are measured. The results show that FCM converges faster and more stable than its counterparts.
Copyright (c) 2021 Sunday Emmanuel Fadugba, Roseline Bosede Ogunrinde, Rowland Rotimi Ogunrinde https://creativecommons.org/licenses/by-sa/4.0
THE STABILITY OF THE CRITICAL POINTS OF THE GENERALIZED GAUSE TYPE PREDATOR-PREY FISHERY MODELS WITH PROPORTIONAL HARVESTING AND TIME DELAY
In the marine ecosystem, the time delay or lag may occur in the predator response function, which measures the rate of capture of prey by a predator. This is because, when the growth of the prey population is null at the time delay period, the predator’s growth is affected by its population and prey population densities only after the time delay period. Therefore, the generalized Gause type predator-prey fishery models with a selective proportional harvesting rate of fish and time lag in the Holling type II predator response function are proposed to simulate and solve the population dynamical problem. From the mathematical analysis of the models, a certain dimension of time delays in the predator response or reaction function can change originally stable non-trivial critical points to unstable ones. This is due to the existence of the Hopf bifurcation that measures the critical values of the time lag, which will affect the stabilities of the non-trivial critical points of the models. Therefore, the effects of increasing and decreasing the values of selective proportional harvesting rate terms of prey and predator on the stabilities of the non-trivial critical points of the fishery models were analysed. Results have shown that, by increasing the values of the total proportion of prey and predator harvesting denoted by qx Ex and qy Ey respectively, within the range 0.3102 ≤ qx Ex ≤ 0.9984 and 0.5049 ≤ qy Ey ≤ 0.5363, the originally unstable non-trivial critical points of the fishery models can be stable.
Copyright (c) 2021 Wan Natasha Wan Hussin, Rohana Embong, Che Noorlia Noor https://creativecommons.org/licenses/by-sa/4.0
MACHINE LEARNING TECHNIQUES FOR EARLY HEART FAILURE PREDICTION
This paper discusses the performance of four popular machine learning techniques for predicting heart failure using a publicly available dataset from kaggle.com, which are Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), and Logistic Regression (LR). They were selected due to their good performance in medical-related applications. Heart failure is a common public health problem, and there is a need to improve the management of heart failure cases to increase the survival rate. The vast amount of medical data related to heart failure and the availability of powerful computing devices allow researchers to conduct more experiments. The performance of the machine learning techniques was measured by accuracy, precision, recall, f1-score, sensitivity, and specificity in predicting heart failure with 13 symptoms or features. Experimental analysis showed that RF produces the highest performance score, which is 0.88 compared to SVM, NB, and LR. Further experiments with RF were also conducted to determine the important features in predicting heart failure, and the results indicated that all 13 symptoms or features are important.
Copyright (c) 2021 Nur Shahellin Mansur Huang, Zaidah Ibrahim, Norizan Mat Diah https://creativecommons.org/licenses/by-sa/4.0
ON A NEW GENERALIZED BETA FUNCTION DEFINED BY THE GENERALIZED WRIGHT FUNCTION AND ITS APPLICATIONS
Various extensions of classical gamma, beta, Gauss hypergeometric and confluent hypergeometric functions have been proposed recently by many researchers. In this paper, further generalized extended beta function with some of its properties like summation formulas, Integral representations, connections with other special functions such as incomplete gamma, classical beta, classical Wright, hypergeometric, error, Fox-H, Fox-Wright, Meijer-G functions are obtained. Beta distribution together with-it corresponding moment, mean, variance, moment generating function and cumulative distribution are also presented. Moreover, the generalized beta function is used to generalized classical and other related extended Gauss, Kumar confluent, Appell’s and Lauricella’s hypergeometric functions with their integral representations, differential, difference, summation and transformations formulas.
Copyright (c) 2021 Umar Muhammad Abubakar, Saroj Patel https://creativecommons.org/licenses/by-sa/4.0
A MIXED INTEGER GOAL PROGRAMMING (MIGP) MODEL FOR DONATED BLOOD TRANSPORTATION PROBLEM – A PRELIMINARY STUDY
Blood Supply Chain (BSC) concerns with flow of blood products from blood collection by donors to transfusion of blood components to patients. BSC comprises of collection, testing, processing, storage, distribution and transfusion activities, which are normally responsibility of Blood Centre and hospitals. In Malaysia, National Blood Centre (PDN) is responsible to organize blood donation, collection and processing. Current procedure practised by PDN is to have vehicles sending staffs and equipment while one vehicle is assigned to collect donated blood from donation sites and transport the blood to PDN within six hours. As consequence, vehicles shortages are encountered and resources optimization unachieved especially when many blood donation sites involved per day. This paper presents the results of a preliminary study which aims at proposing blood collection optimal routes for blood collecting vehicles that adhere to all pre-determined time windows for blood collection at blood donation sites. A Mixed Integer Goal Programming (MIGP) model based on Vehicle Routing Problem with Time Windows (VRPTW) has been formulated. The MIGP model pursues four goals, namely, to minimize total distance travelled, to minimize total travel time, to minimize total waiting time of vehicles and to minimize number of vehicles (routes). The model wassolved using preemptive goal programming approach and existing heuristics for the VRPTW. Based on the results, it can be concluded that the donated blood can be collected and transported using reduced number of vehicles as proposed by the MIGP model’s optimal compared to the total number of vehicles used by current practice, Thus, the proposed VRPTW based MIGP model has promising significant impact for donated blood transportation in terms of resources optimization and costs savings. The model and approach could be easily extended to solve larger problem involving large number of donation sites with variants of time windows for the sites.
Copyright (c) 2021 Adibah Shuib, Puteh Maisarah Ibrahim https://creativecommons.org/licenses/by-sa/4.0
ELECTRONIC PARTICIPATION INITIATIVES IN AFRICA: EXTRACTION FROM LITERATURE REVIEW PERSPECTIVES
The main objective of this research is to examine the use of Information & Communication Technologies (ICT) and derive the success factors of eParticipation initiatives according to the electronic government (eGovernment) content evaluation. For this purpose, an African study took place using a two-step methodological technique: ‘review of the United Nations’ eGovernment survey report; and a wide review from the existing literature. The results suggest that the tools and technologies presently applied are mainly general purpose and not exactly designed for eParticipation. In addition, the results indicated that success factors can be categorized into seven groups of factors: government commitment; usability; combination of both online and offline networks; comprehensive communication and encouraging plan; security and privacy, organizational issues; and topics complexity. A comparison with reviewed factors of eParticipation initiatives success suggests there are similarities and significant differences on ICT usage among countries. This study anticipates that its results could be of interest to practitioners as it highlights some knowledge in a practical way. Furthermore, this research could be of interest to researchers as it contributes in validating eParticipation evaluation models.
Copyright (c) 2021 Isyaku Uba Haruna, Maslinda Mohd Nadzir, Hapini Awang https://creativecommons.org/licenses/by-sa/4.0
IMPLEMENTATION OF BLOM’S KEY PREDISTRIBUTION SCHEME BY USING ELLIPTIC CURVE CRYPTOGRAPHY
Cryptography, along with its various methods is used to serve the security communication purpose. Cryptography is said to be secure if the encryption key is hard to break by the attacker. Initially, Blom’s Key pre-distribution uses an integer finite field which makes this scheme easy to be intervened by attackers and criminal activists. Hence, this study suggests implementing the Elliptic Curve Cryptography to better enhance the security of the original Blom’s. In this proposed scheme, points generated from the elliptic curve will be appointed as public identifiers to be used in the original scheme. The private key and session key of each user are generated using the addition law mathematical operation with public identifiers assigned. Two users who intend to communicate with each other will obtain a common session key. Overall, the modification of Blom’s Key pre-distribution scheme will be presented in this study.
Copyright (c) 2021 Md Nizam Udin, Farah Azaliney Mohd Amin, Aminah Abdul Malek, Nur Annisa Zulkifili, Nur Atiqah Ghazali, Siti Aisyah Mohd Ridzuwan https://creativecommons.org/licenses/by-sa/4.0
EFFECTIVE ROUTING PROTOCOL FOR WIRELESS SENSOR NETWORKS USING CRT-LEACH ALGORITHM
Wireless Sensor Networks (WSNs) consist of huge number of sensor nodes dispersed in a domain of enthusiasm with at least one sink for watching the environment and physical situation. These sensor hubs are circulated in threatening conditions and are unprotected to deficiencies, for example, power dissemination, equipment glitches, communication link errors and malicious attacks, among others. It has been established that essentialness, speed and unwavering quality are the chief test in the usefulness of WSNs as they are controlled with compelled imperativeness and restricted equipment assets. Accordingly, it is necessary to structure vitality proficient steering conventions for WSNs applications. Chinese Remainder Theorem (CRT)- based packet splitting integrated with Low-Energy Adaptive Clustering Hierarchy (LEACH) algorithm routing protocol was proposed so as to decrease vitality utilization during correspondence and improve message dependability in WSNs. The consequences of exploratory reproductions show that the proposed structure delivered powerful directing convention for WSNs when contrasted with existing routing protocols to the extent essentialness usage, speed, equipment necessities and transformation delay continuously WSNs.
Copyright (c) 2021 Kamaldeen Ayodele Raji, Ayisat Wuraola Asaju-Gbolagade, Kazeem Alagbe Gbolagade https://creativecommons.org/licenses/by-sa/4.0
ENVIRONMENTAL DETERMINANT OF RURAL HOUSEHOLDS’ VULNERABILITY TO WATER SCARCITY IN SEMI-ARID NIGERIA
Though water resource is an important natural endowment for the attainment of improved living conditions and sustainable development, man’s consumptive demand for it continued to fall short of the supplies. This is especially in the rural communities of the semi-arid region of Nigeria, whose most water sources are unimproved and the supplies from which fall below the demand, resulting in water scarcity. The study assessed the environmental perspective of the rural households’ susceptibility to water scarcity in Nigeria. The study employed multistage sampling, the mixed-method approach of data sourcing, and the data were analyzed with the basic descriptive and inferential statistical tools. The study findings show the continued practice of extended family system, characterized with low literacy rate and weak income levels, use of shared latrines, as well as defecation in the bushes and open spaces. It also demonstrated nine-in-ten households are vulnerable to the complex conditions of water scarcity, especially those in Kerri-Kerri Formation and the Fika Shale, as the Pearson correlation of the conditions revealed. The linear regression model of the correlated variables statistically proved to have accounted for more than 60% of the water crisis. However, the households’ adaptation strategies to water scarcity were traditional and highly ineffective to the problem of water scarcity conditions of the area. This denotes that the environmental variability influences both water availability and vulnerability to scarcity. Therefore, the study recommends the mainstreaming of the physical environmental conditions into the rural water policy, in addition to upgrading and maintenance of the available water facilities.
Copyright (c) 2021 Mohammed Baba-Adamu, Ibrahim Ahmed Jajere https://creativecommons.org/licenses/by-sa/4.0
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