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
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
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) 2025 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) 2025 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) 2025 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) 2025 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) 2025 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) 2025 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) 2025 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) 2025 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) 2025 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) 2025 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 Zuraida Khairudin 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) 2025 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
THE HYBRIDIZATION OF ANALYTIC HIERARCHY PROCESS AND GRAPH THEORY MATRIX APPROACH (AHP-GTMA) TO SOLVE SOLID WASTE TRANSSHIPMENT SITE SELECTION PROBLEM
Proper selection of solid waste facilities is a complex issue and requires a broad assessment measure. Hence, the selection of these sites is a multiple criteria decision-making issue. This study proposed a hybridization of the Analytic Hierarchy Process and Graph Theory Matrix Approach (AHP-GTMA) method in selecting the best solid waste transshipment site. The objectives of this study are to apply the AHP-GTMA method to solve the solid waste transshipment site selection problem, and to determine the best solid waste transshipment site. Real-life empirical data about the solid waste transshipment site selection in Istanbul were used to demonstrate the application of the AHP-GTMA method. The data consisted of five alternativ es of the solid waste transshipment site candidates in Istanbul labeled as A1, A2, A3, A4, and A5, and five criteria which were proximity to industrial solid waste (C1), proximity to household solid waste (C2), transportation simplicity (C3), necessity (C4) and proximity to residential area (C5). The findings revealed the ranking order of the criteria was C4 > C1 > C2 > C3 > C5 and the ranking order for the alternatives was given by A1 > A5 > A3 > A4 > A2. In conclusion, AHP-GTMA is successfully applied to solve the solid waste transshipment site selection problem.
Solid Waste Transshipment Site Selection
Copyright (c) 2025 Wan Nurdini Mior Abd Halim, Ilyana Farhin Mohd Hanafi, Nurul Izzati Johari, Nor Faradilah Mahad https://creativecommons.org/licenses/by-sa/4.0
A DYNAMIC SIR MODEL FOR THE SPREAD OF NOVEL CORONAVIRUS DISEASE 2019 (COVID-19) IN MALAYSIA
The emergence of the first coronavirus disease 2019 (COVID-19) case in Malaysia has increased the number of infected cases. Hence, this study proposes a Susceptible-InfectedRecovery (SIR) epidemiological model of the COVID-19 epidemic to portray the outbreak's situation. The SIR model is numerically solved using the Fourth-order Runge-Kutta (RK4) method in Matlab®. The Euler method verifies that the graphical results of the SIR model are reliable and valid. In addition, this study analyses the stability of disease-free and endemic equilibriums of the SIR model by the Jacobian matrix. The results show the outbreak for phase 1 occurs in the first 100 days of the phase due to the increased infected cases in early March 2020. As for phase 2, the increases of infected cases in wave 2 make the outbreak occur throughout phase 2, with R0 being higher than phase 1. The infected population for phase 3 shows asymptotic behavior even though the infection rate increases, but the recovery rate is much higher than in phase 2. The local stability of the endemic equilibrium of all phases exists since the value of R0 is more than one. The system is locally asymptotic stable for all three phases since the obtained eigenvalues are real and negative.
Copyright (c) 2025 Nur Aziean Mohd Idris, Siti Khadijah Mohtar, Zaileha Md Ali, Khadijah Abdul Hamid https://creativecommons.org/licenses/by-sa/4.0
THE EFFECTIVENESS OF MOVEMENT CONTROL ORDER (MCO) PHASES IMPLEMENTATION IN MALAYSIA
The COVID-19 cases have taken its strike since December 2019 in Wuhan, China and then has spread to other countries. Malaysia was not excluded from this pandemic, in which the first case of COVID-19 was discovered on 25th January 2020. The disease has caused a pandemic outbreak ever since, driving many countries to close international gateways and implement lockdowns and many studies have been trying to model and forecast the rising number of Covid-19 cases worldwide. This study aimed to prove the effectiveness of Movement Control Order (MCO) phases implemented in Malaysia. From 8 MCO phases implemented in Malaysia, 5 of the MCO phases were taken into consideration for this study. Line plots were used to observed the pattern, increasing (+) or decreasing (-) number of cases for each MCO implementation. The before and after trends for each phase were recorded and tabulated. Using the Wilcoxon Signed-rank test with Binomial probability calculation showed the p-value=0.001< α=0.05 obtained. Hence, supported the hypothesis that MCO implementation reduces the number of positive cases in Malaysia. In conclusion, the implementation of lockdown would ‘flatten the curve’ of the daily COVID-19 cases, resulting in better control over the spread of the COVID-19 virus in Malaysia.
Copyright (c) 2025 Hezlin Aryani Abd Rahman, Muhammad Akram Rahman, ‘Ain Najihah Rozaimi, Irfan Bihar Zulnahar https://creativecommons.org/licenses/by-sa/4.0
DETERMINING OPTIMAL TRANSPORTATION ALLOCATION USING LINEAR PROGRAMMING METHODS
The transportation problem is a subset of the broader linear programming (L.P.) technique developed to assist managers in making decisions. It has been used in real-life applications to minimise total transportation costs by satisfying destination and source requirements. The study aimed to minimise the delivery of items from the plant to the dealer's location by determining the optimal allocation of inventory from suppliers to dealers in the Malaysian cities of Seremban, Johor Bahru, Kuala Lumpur/Selangor and Penang. Meanwhile, the specific objectives of the study were: (i) to formulate transportation problems using the L.P., (ii) to identify a basic feasible solution (BFS), and (iii) to do an optimality analysis. A case study of an operations research problem involving the optimisation of distribution costs and the effective delivery system of inventory by utilising transportation models, namely the North-West Corner Rule (NWCR), Simplex method, and Vogel's Approximation Method (VAM) are presented and discussed. The efficiency and effectiveness of the transportation problem algorithm are determined using the Excel Solver. The excel solution and sensitivity analysis provide information about the optimal distribution strategy for their products. The results indicated that the delivery cost is reduced when the transportation problem algorithm is used. It saved up to RM50,605.21, or 78.91 per cent of the cost, and it can be concluded that the profit margin is higher than with the previous strategies. Among all transportation models, VAM and Simplex methods are considered the best methods since they produce the ideal results, whereas the North-West Corner Rule is considered the simplest method but produces the worst results. In conclusion, this study could assist the distributors in reducing shipping costs and ensuring that the inventory is distributed efficiently.
Copyright (c) 2025 Tengku Mohd Hazimi Tuan Hassan, Maslin Masrom https://creativecommons.org/licenses/by-sa/4.0
PREDICTION OF MALAYSIAN WOMEN DIVORCE USING MACHINE LEARNING TECHNIQUES
This paper discusses the performance of three machine learning techniques namely Decision Tree, Logistic Regression and Artificial Neural Network for predicting divorce among Malaysian women. Secondary data were obtained from the Fifth Malaysia Population and Family Survey (MPFS-5) conducted by the National Population and Family Development Board (LPPKN). The total number of instances in the dataset was 7,644 ever married Malaysian women aged 15 to 59 years old. Divorce is currently a serious problem among the Malaysian community due to various reasons. In 2019, the divorce rate in Malaysia rose by 12% from the previous year. During the first three months of the Movement Control Order (MCO), i.e. from March 18 to June 18, 2020, the Syariah Court of Malaysia recorded 6,569 divorce cases. Worse, a total of 90,766 divorce cases were recorded from January to October 2020. Six predictive models were used for comparison, namely Decision Tree (C5.0 and CHAID), Logistic Regression (Forward Stepwise and Backward Stepwise), and Artificial Neural Network (Multi-Layer Perceptron and Radial Basis Function). Among the six predictiv methods, the Decision Tree model (C5.0) was found to be the best model in classifying divorce among Malaysian women. The accuracy of the C5.0 model was 77.96% followed by the Artificial Neural Network (Multi-Layer Perceptron) and Logistic Regression (Forward Stepwise) model (74.68% and 67.89%, respectively). The order of important predictors in predicting divorce among Malaysian women is the wives’ employment status (0.1531) followed by the husbands’ employment status (0.1396), type of marriage (0.1327), race/ethnicity (0.1327), distant relationship (0.1212), the wives’ qualification level (0.1115), age group (0.1053) and religion (0.0998).
Copyright (c) 2025 Nazim Aimran, Adzhar Rambli, Asyraf Afthanorhan, Adzmel Mahmud, Azlin Sapri, Airena Aireen https://creativecommons.org/licenses/by-sa/4.0
MEASURING CUSTOMER PERCEPTION TOWARDS ROAD TRANSPORT DEPARTMENT BRANCHES BASED ON CONTENT ANALYSIS
Customer perception is an important element for all service providers including government office. The road transport enforcer, Road Transport Department (RTD) Malaysia always ensures that the customers are satisfied with their services especially at their service counters throughout the country. Customer perception is shaped by multiple variables including direct and indirect interaction offered by government office like RTD. In this study, customers’ perception towards RTD Perak state is explored. This includes all six branches within Perak state which are UTC Ipoh branch, Taiping Branch, Manjung Branch, Tapah Branch and Teluk Intan Branch, based on google maps review star rating. Details analysis on the comments left by the customers is done using content analysis and correlation analysis. Result shows the positive impact for the RTD especially and all public servant agencies generally.
Copyright (c) 2025 Wan Aqmal Azhar W Abd Aziz, S.Sarifah Radiah Shariff, Wan Mazlina Wan Mohamed https://creativecommons.org/licenses/by-sa/4.0
RESTORATION OF OLD MALAY JAWI MANUSCRIPTS USING MUMFORD-SHAH AND BERTALMIO INPAINTING MODELS
Jawi is the earliest writing script of the Malay Archipelago that was derived from Arabic letters. Old Malay Jawi manuscripts may provide vital information about the legacy, cultures, and historical evolution of the Malay Archipelago over time. However, while preserved, old Malay Jawi manuscripts tend to be damaged. Image inpainting is a process of reconstructing missing parts of an image which can be used to restore the old Malay Jawi manuscripts. The Mumford-Shah and Bertalmio inpainting models are two well-known and effective methods for solving the image inpainting problem. Hence, the aim of this study is to determine which model is better at restoring corrupted input images of the old Malay Jawi manuscripts. A sample of thirty (30) old Malay Jawi manuscript images were obtained from Kumpulan Penyelidikan Etnomatematik Melayu (KUPELEMA). The corrupted images were restored using both models, implemented using the MATLAB software. The Structural Similarity Index Measure (SSIM) and Mean Absolute Error (MAE) were utilized to assess the quality of the results. The numerical experiment demonstrates that the average values of SSIM and MAE for Mumford-Shah inpainting model are 0.9380 and 0.0151 respectively, while the values for the Bertalmio inpainting model are 0.8762 and 0.0255 respectively. This indicates that the Mumford-Shah inpainting model is more effective than Bertalmio inpainting model. The algorithms used in this study can be upgraded to a software framework for commercial use and can be implemented for other kinds of digitized data.
Copyright (c) 2025 Nurul Nabilah Zainal, Nur Fatini Mohammad Yuri, Abdul Kadir Jumaat https://creativecommons.org/licenses/by-sa/4.0
AN IMPROVEMENT OF RESOURCE CONSUMPTION IN WIRELESS SENSOR NETWORK (WSN) USING COMPRESSIVE SENSING
Wireless Sensor Network (WSN) refers to a group of spatially dispersed and dedicated sensors designed to monitor and record the physical conditions of the environment and organise the data collected at a central location. One of the problems in wireless sensor network (WSN) is the low computational resources available which is the limited battery life of sensor nodes. Hence, one of the ways to prolong the lifetime is to implement Low energy Adaptive Clustering Hierarchy (LEACH). The Compressive Sensing (CS) algorithm has been implemented to the network through Cluster Head (CH) to further the lifetime longevity. CS is a technique utilised for energy efficient data gathering in WSNs. Compressive sensing provides benefits such as robustness, log lifetime of the network, reduced energy consumption, and a simple routing scheme. The simulation testing and algorithm implementation have been done in MATLAB with the scripting language. Through the implementation of CS, it can be concluded that the CS algorithm helps increase the network lifetime by 9.7%.
Copyright (c) 2025 Maizatul Akmal Ibrahim, Norkhushaini Awang https://creativecommons.org/licenses/by-sa/4.0
TASKFORCE FRAMEWORK IN VALIDATING AUTHENTICITY OF UNIVERSITY INFORMATION OVER THE WIKIPEDIA
Wikipedia is considered a globally primary resource for public review. As it is the largest, free, and most popular general reference work on the World Wide Web, it is easy for web crawlers to do their search indexes and improve the visibility of the page in the digital world. To sustain its top ranking in organic search and display of any search engines, Wikipedia attracts many links and contains a large amount of content on a broad set of topics. Document about Universiti Teknologi MARA (UiTM) started in Wikipedia since 2004, and it has been consistently evolving since then. UiTM has many branch campuses throughout the nation. It is Malaysia's largest institution of higher learning in terms of size and population, with the anchor campus in Shah Alam and one in each state. Whilst the main university is visible digitally, the branch campuses are almost invisible digitally. These may lead to inauthentic content and dispute of affiliation among the UiTM branches. This paper discusses the approach taken to overcome the problem of the authenticity of facts about UiTM. Content analysis is deployed throughout all related campus pages found on Wikipedia. A table of guideline was developed to form an authenticity checklist.
Copyright (c) 2025 Hayati Abd Rahman, Zainab Mohd Noor, Amirul Abd Rashid, Roziah Mohd Janor https://creativecommons.org/licenses/by-sa/4.0
TOWARDS DEVELOPMENT OF ROBUST MACHINE LEARNING MODEL FOR MALAYSIAN CORPORATION: A SYSTEMATIC REVIEW OF ESSENTIAL ASPECTS FOR CORPORATE CREDIT RISK ASSESSMENT
This study aims to review the essential aspects of credit risk assessment. The scope of this study is the credit risk assessment studies that used machine learning or used Malaysian data. This study is an overview of the development of robust machine learning for Malaysian corporation credit risk assessment. This study used a systematic review as the methodology. After thorough searching, this study has selected 20 studies to be reviewed. As a result, three essential aspects are idetified: the variables, the features, and the methods used for financial distress prediction. This study found that financial ratios, macroeconomics, and corporate governance indicators are essential in credit risk assessment studies. The debt ratio was recorded as the most widely used ratio, found in 14 studies, followed by the liquidity ratio, used in 12 studies. In addition, the studies performed using Malaysian data show that the debt ratio and liquidity ratio are significant. Support vector machine (SVM) and genetic algorithm (GA) are among the best methods to be used. Recurrent neural network (RNN) is the latest credit risk assessment method to solve the time series data problem. In conclusion, all the essential aspects identified in this study should be considered in any credit risk assessment study
Copyright (c) 2025 Zulkifli Halim, Shuhaida Mohamed Shuhidan https://creativecommons.org/licenses/by-sa/4.0