Mathematical Sciences and Informatics Journal
MIJ is indexed in MyCite and DOAJ. The Mathematical Sciences and Informatics Journal (MIJ) (eISSN: 2735-0703) is a peer review , open-access international refereed journal. MIJ is published twice a year (May and November). MIJ accepts research papers in the field of Mathematics, Statistics, Actuary, Informatics and Computing. MIJ was initiated by researchers from the Faculty of Computer and Mathematical Sciences, UiTM Perak Branch, Tapah Campus in collaboration with other researchers from local and international universities. This journal is published by UiTM Press under the coordination of the Research Management Center (RMC), Universiti Teknologi MARA. MIJ is indexed in MyCite and DOAJ. MIJ is also available at Google Scholar. More about MIJ, feel free to visit our official website at https://mijuitm.com.my. CLICK HERE TO SUBMIT MANUSCRIPT
Identifying Barriers to Lean–Intelligent Construction in the Project Phase: A Review
The integration of Lean Construction principles and Intelligent Construction Technologies (ICT) offers transformative potential for enhancing efficiency, minimizing waste, and improving decision-making throughout the construction lifecycle. However, adoption remains constrained by complex technical, financial, environmental, and social barriers. This study conducts a Systematic Literature Review (SLR) using the PRISMA protocol to identify and classify key barriers hindering Lean–Intelligent Construction implementation. Twenty-eight peer-reviewed articles published between 2021 and 2025 were analyzed, with barriers mapped across project phases and lean waste categories. The findings reveal that technical and social barriers are the most dominant, particularly during the design and construction phases, leading to critical waste types such as waiting, defects, and over-processing. Financial and environmental barriers show more localized effects, primarily linked to inventory and motion waste during procurement and construction. The Sankey diagram visualization demonstrates that waste generation in intelligent construction is not isolated but emerges from the interaction of multiple barriers across project stages. This study contributes to a deeper understanding of how digital and organizational inefficiencies propagate lean waste, providing a foundation for targeted interventions in different project phases. The findings emphasize the need for holistic strategies that combine technological innovation, organizational change management, and policy reform to achieve sustainable digital transformation in the construction sector.
Copyright (c) 2026 Ferry Firmawan https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Learning-Based Transfer Learning Approach for Acute Ischemic Stroke Classification Using Magnetic Resonance Imaging
Acute ischemic stroke (AIS) is a leading cause of mortality and morbidity worldwide, accounting for approximately 85% of all stroke cases. Early and accurate diagnosis using Magnetic Resonance Imaging (MRI) is crucial for timely intervention and improved patient outcomes. However, manual interpretation of MRI images is time-consuming and requires specialized expertise.This study aims to develop and evaluate deep learning-based transfer learning models for automated classification of AIS using MRI images, specifically comparing the performance of VGG-16, ResNet50, InceptionV3, and VGG-19 architectures. A dataset comprising 2,400 MRI brain images (1,200 AIS cases and 1,200 normal cases) collected from multiple hospitals in Indonesia was utilized. Images were preprocessed, resized to 128×128 pixels, and augmented using rotation, zooming, shifting, and flipping techniques. The dataset was split into 80% training, 10% validation, and 10% testing sets. Four pre-trained Convolutional Neural Network (CNN) models were fine-tuned and evaluated using accuracy, precision, recall, and F1-score metrics. VGG-16 achieved the highest performance with an accuracy of 96.5%, followed by InceptionV3 (94.2%), ResNet50 (93.8%), and VGG-19 (95.1%). The VGG-16 model demonstrated superior capability in feature extraction and classification, with minimal overfitting. Transfer learning with VGG-16 architecture provides an effective and efficient approach for automated AIS classification from MRI images, offering significant potential to assist radiologists in rapid and accurate diagnosis.
Copyright (c) 2026 Andi Kurniawan Nugroho https://creativecommons.org/licenses/by-nc-sa/4.0
A Multimodal Swarm-Based Multi-Agent Retrieval-Augmented Generation (RAG) For Domain-Specific Applications
The increasing complexity of medical data, encompassing text, images, and documents, demands advanced systems capable of integrating and analyzing multimodal inputs to support clinical decision-making. This study presents a novel multimodal AI system designed for medical information retrieval and decision support, utilizing swarm-based agents and retrieval-augmented generation (RAG) to process diverse data types concurrently. The system employs collective intelligence-inspired swarm agents to distribute tasks efficiently and RAG to enhance contextual relevance. It achieved strong semantic understanding on dental health queries with BERTScore F1 values of 0.8338 in query with image upload scenario, though it exhibited challenges in text-image alignment with MM-Align score of 0.3103 and factual consistency of 0.4737. In query-only scenario, dental-related queries have high BERTScore F1 values of 0.9548 and mostly consistent facts of 0.950. While for non-dental queries, the system leveraged web search and give current information as result. The comparative analysis results highlight its adaptability across medical domains, despite limitations in aligning multimodal inputs and ensuring complete factual accuracy. These findings underscore the potential of swarm-based multimodal AI to improve healthcare outcomes by providing contextually relevant analyses while identifying key areas for further refinement.
Copyright (c) 2026 Amelia Ritahani Binti Ismail https://creativecommons.org/licenses/by-nc-sa/4.0
A Contactless Computer Vision System for Underwater Walking and Jogging Gait Analysis Using YOLO-Pose and Multi-CNN BiLSTM Architecture
Buoyancy-assisted hydrotherapy exercise has been proved to reduce joint loading and accelerates functional recovery. However, conventional marker or sensor-based approaches are found costly and impractical for underwater use due to water interference and setup constraints for recovery progress monitoring. To overcome these challenges, a computer vision-based gait analysis model was trained for jogging sessions in hydrotherapy pools. This paper used 2D coordinates extracted using You Only Look Once (YOLO) 11m-pose as the model input without noise filtration to validate the its robustness. A comparison of hyperparameter optimization algorithms was also conducted, with the combination of Multivariate Tree-structured Parzen Estimators (MultiTPE) and Hyperband identified as the optimal approach. Two Convolutional Bidirectional Long Short-Term Memory architectures, i.e., single vs. multi convolutional layers (CNNs) per pooling were applied and compared in both multi-head and single-head regression settings. Result found that multi-CNNs per pooling with multi-task learning best exploit inter-parameter correlations. On a 45-samples test set evaluation, the model achieved an Intraclass Correlation Coefficient (ICC) with two-way random effects, absolute agreement, single rater model (ICC(2,1)) of 0.8999, Pearson’s Correlation Coefficient (PCC) of 0.9066, Mean Absolute Error of 0.0954 seconds for swing, stance, and stride time, while 3.5141 steps/min for cadence. The developed system is able to achieve precise analysis for underwater leg movements.
Copyright (c) 2025 Masurah Mohamad https://creativecommons.org/licenses/by-nc-sa/4.0
A Bibliometric Analysis of Human-Agent Collaboration in Empathetic Social Agents: Trends, Influences, and Research Frontiers
The rapid growth of artificial intelligence (AI) and affective computing has intensified interest in empathetic social agents such as chatbots, robots, and virtual assistants. These agents are increasingly applied in healthcare, education, and customer service, yet research on human–agent collaboration (HAC) remains fragmented. This bibliometric study maps and analyses the intellectual structure, key contributions, and emerging frontiers of HAC in empathetic social agents from 2020 to 2025. A dataset of 1,164 Scopus-indexed publications was analysed using Scopus Analytics and VOSviewer to examine publication trends, citation networks, and keyword patterns. Results show rapid growth peaking in 2024, with computer science and engineering forming the technological core, complemented by psychology, social sciences, and medicine. Citation analyses identify seminal works anchoring the field, while keyword mapping highlights established themes (HRI, social robotics, AI ethics), evolving topics (ChatGPT, generative AI, user experience), and underexplored areas such as AI intimacy, error management, and therapeutic applications. Overall, the field is consolidating around technical, psychological, and ethical dimensions while shifting toward generative AI and affect-aware collaboration, providing a roadmap for advancing both theory and practice.
Predicting Real Estate Prices with AI: A Comparative Study of Machine Learning Models
Accurate house price prediction is vital for economic, financial, and policy decision-making, impacting homebuyers, investors, financial institutions, and government agencies. This study employs a data-driven machine learning approach to forecast residential property prices, with a particular focus on high-rise properties in Kuala Lumpur. Real-world housing data based on 12,735 transactions from 2021 to August 2024, were collected from the National Property Information Centre (NAPIC), pre-processed, and analysed using Exploratory Data Analysis (EDA) to understand the influence of various property attributes on prices. Multiple predictive models, including traditional regression, ensemble methods (Random Forest, Gradient Boosting Machine), and deep learning (Artificial Neural Networks), were developed and rigorously compared. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²) on an 80:20 training-testing split. Hyperparameter tuning and K-fold cross-validation were applied to optimize accuracy, prevent overfitting, and ensure model generalizability. The Random Forest model emerged as the optimal predictor, demonstrating robust performance with the lowest error values and highest R² score compared to other tested algorithms. This research provides practical insights into key price-influencing features and highlights the efficacy of machine learning for robust and interpretable house price prediction in the Malaysian real estate market.
Copyright (c) 2025 Khairulliza Ahmad Salleh https://creativecommons.org/licenses/by-nc-sa/4.0
Identifying Risk Identification Towards Halal Products Among Youngsters
Halal products are not just for consumption. Modest clothes, halal media and entertainment, halal logistics and supply chains, and other sectors that cater to Muslim customers worldwide are among them. There are three main objectives driving this study. In the beginning, it aims to pinpoint the many elements of young people's risk consciousness with reference to halal products. The research makes an effort to strategically combine qualitative and quantitative investigations in order to build a comprehensive risk register. The researchers' final goal is to develop a risk management matrix with a focus on halal goods. The risk management matrix offers a methodical approach to identifying and reducing the risks associated with halal goods. The study searches for possible risk scenarios, evaluates how they can affect halal products, and takes likelihood into account. Their work led to the creation of a risk management matrix, which lists risks in order of decreasing risk and increasing risk based on available mitigation methods. It is noteworthy that halal-certified products, which sometimes cost more than their non-certified counterparts, have a higher exposure to risk ratings (0.76). The study emphasizes the value of raising young consumers' awareness of halal products and highlighting the risks associated with their choice of halal goods.
Copyright (c) 2025 ANIS ZAFIRAH AZMI, AHMAD NUR AZAM AHMAD RIDZUAN, NOR FAEZAH MOHAMAD RAZI, SITI NUR AISYAH AZEMAN https://creativecommons.org/licenses/by-nc-sa/4.0
Generalized Explicit expressions for 2nd order Recurrences
This article investigates the flexibility of closed-form generalized solutions for complex second-order linear recurrence relations. We derive and examine foundational results, including the generating function and a generalized Binet-type formula, in their most comprehensive forms. These tools are then used to establish new identities. Furthermore, we extend our results to generalize classical identities—such as those of Cassini, Catalan, Vajda, and D’Ocagne—for the first n terms of such recurrences.
Copyright (c) 2025 Dr. K. L. Verma https://creativecommons.org/licenses/by-nc-sa/4.0
Exploring Graph Isomorphism of Pucuk Rebung Ornaments
Pucuk Rebung ornaments are widely recognized in Malaysia, particularly in traditional textiles such as songket, batik sarong, and limar cloth. The Pucuk Rebung pattern has many variations across the Southest Asia, particularly in Malaysia, Indonesia and Thailand. In this study, Pucuk Rebung patterns from Malaysia are highlighted for four different pattern variations through graph isomorphism. A major challenge in studying traditional motifs is the lack of a systematic approach to quantify and compare their structural similarities. Traditional analyses rely heavily on visual interpretation, making it difficult to determine the uniqueness of each pattern mathematically. To address this issue, this study employs a graph-theoretical approach by transforming Pucuk Rebung patterns into zero-one matrices through image processing. Then, the corresponding graphs based on the zero-one matrices are created, and analyzing their structural similarities using graph isomorphism. The findings suggest that no Pucuk Rebung patterns are isomorphic, which confirms the originality of each Pucuk Rebung pattern. Through the study, it emphasises the efficacy of graph theory in mathematically distinguishing traditional motifs, thereby facilitating a more profound comprehension of their structural characteristics through the context of mathematical theory.
Copyright (c) 2025 MASNIRA RAMLI, JURIATY ILMA ZAILANI, WAN NURUL HUSNA WAN NORDIN https://creativecommons.org/licenses/by-nc-sa/4.0
Forecasting Stock Market Prices using Geometric Brownian Motion by Applying the Optimal Volatility Measurement
Investing in the Malaysian stock market can be overwhelming due to the abundance of possibilities, necessitating informed decision-making to navigate the volatile market. This study addresses a common problem faced by investors venturing into the stock market, where instability and fluctuations pose significant risks, leading to financial losses stemming from inadequate knowledge about suitable stocks for investment. Unlike many studies that focus on long-term forecasting methods, this research adopts the Geometric Brownian Motion (GBM) model for short-term investment analysis. The study aims to identify the most effective volatility measurement model, develop a forecasting model using GBM based on the chosen volatility model, and evaluate the accuracy of the GBM model using Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Deviation (MAD). Four volatility models, which include simple, log, high-low, and high-low-closed volatility are analysed to determine the most effective volatility measurement model. Four months of daily stock data are collected to ensure accuracy excluding factors such as seasonality, politics, natural disasters, and wars. Findings indicate that the simple volatility model is the most suitable for forecasting stock market trends using the GBM model, demonstrating high accuracy based on MSE, MAPE and MAD.
Copyright (c) 2025 Siti Nazifah Zainol Abidin, Farah Syahida Fauzi, Sabihah Maisarah Sahrudin , Nur Asyikin Abdullah , Siti Maisarah Md Zain https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing MCDM with q-Rung Orthopair Fuzzy Dombi Generalized Heronian Mean
This paper presents a set of Dombi generalized Heronian mean operators for q-rung orthopair fuzzy numbers (qROFNs) and proposes a multi-criteria decision-making (MCDM) method based on these operators. The operational rules of qROFNs, grounded in the Dombi t-conorm and t-norm, are presented. A q-rung orthopair fuzzy Dombi generalized Heronian mean (qROFDGHM) operator is formulated based on these principles. A method for addressing MCDM problems utilizing qROFNs and the developed operators is proposed.
Copyright (c) 2025 ZAHARI MD RODZI https://creativecommons.org/licenses/by-nc-sa/4.0
Bipolar pythagorean neutrosophic hamacher heronian mean operators: a new framework in MCDM
This paper introduces the integration of Bipolar Pythagorean Neutrosophic Sets (BPNS) with advanced aggregation operators, namely the Hamacher and Heronian means. These integrations aim to improve the representation and synthesis of decision-making criteria. To this end, a novel framework called the Bipolar Pythagorean Neutrosophic Set-Generalized Hamacher Heronian Mean (BPNS-GHHM) is proposed. The proposed method is shown to satisfy all relevant properties
Hesitant Triangular Fuzzy Generalized Geometric Heronian Mean in MCDM
This paper introduces a new aggregator the Hesitant Triangular Fuzzy Generalized Geometric Heronian Mean (HTFGGHM). The hesitant triangular fuzzy set (HTFS) combined with the GGHHM, makes the HTFGGHM operator capable of ensuring reasonable aggregation through desirable indexes such as idempotency monotonicity and boundedness. As it will be shown, it manages to retain the inherent uncertainty and correlation of the criteria while offering clear and coherent rankings. Incorporating the carefulness of hesitant fuzzy sets and the compute-intensive power of GGHM, the HTFGGHM operator improves the decision accuracy and hence serve as handy tool to tackle vague situation in a multi-attribute decision-making process.
Copyright (c) 2025 Wan Syaidatul Izzati, Zahari Md Rodzi https://creativecommons.org/licenses/by-nc-sa/4.0
A Data-Driven SIR Model Analysis of COVID-19 Interventions in Malaysia
By the end of 2019, COVID-19 began spreading worldwide, posing a significant challenge to effective outbreak management. This study employs the Susceptible-Infected-Recovered (SIR) model to understand the transmission dynamics of COVID-19 in Malaysia, with a population of 34.3 million. Focusing on key phases implemented like the Movement Control Order (MCO) and Recovery Movement Control Order (RMCO), the research evaluates infection rates, recovery dynamics, and reproduction numbers using real-world data from the Johns Hopkins University COVID-19 Dashboard. During the MCO phase (18 March 2020 to 3 May 2020), the transmission rate was 0.0806, the recovery rate was 0.0309, and the basic reproduction number (R₀) was 2.607, with 90.52% of the population remaining susceptible post-phase. The RMCO phase (10 June 2020 to 31 March 2021) saw reduced transmission and recovery rates of 0.0880 and 0.0518, respectively, resulting in an R₀ of 1.698 and 68.97% of the population remaining susceptible. The peak infection rate during RMCO was significantly lower (1.698%), with the infection peak forecasted for 11 December 2020. These findings highlight the effectiveness of phased interventions in mitigating the epidemic's impact while emphasizing the SIR model's utility in providing timely insights during evolving public health crises
Copyright (c) 2025 Abdul Basit, JASNI MOHAMAD ZAIN, HAFIZA ZOYA MOJAHID, ABDUL KADIR JUMAAT, NUR'IZZATI HAMDAN https://creativecommons.org/licenses/by-nc-sa/4.0
Performance Analysis and Implementation of Hybrid Conjugate Gradient Method in Electromagnetic Tomography
Electromagnetic tomography (EMT) is a type of electrical tomography based on electromagnetic induction. Reconstructing images with EMT involves solving inverse problems, which are often poorly defined due to limited prior information about imaging features. Optimization methods such as conjugate gradient (CG), Quasi-Newton, and Steepest Descent can help minimize these problems. The conjugate gradient (CG) algorithm is an iterative method that efficiently handles equations with multiple inputs, saving time but requiring more memory. In this research, four hybrid CG methods which is MN-LAMR, MN-FR, MN-LS and MN-PRP are used to reduce the number of iterations (NOI) and CPU time, achieving excellent numerical performance. Based on numerical performances, MN-LAMR is the best method. Then, MN-LAMR is implemented into the EMT system to produce a new system called EMT-CG. The efficiency of the EMT and EMT-CG systems is evaluated based on error analysis using root mean square error (RMSE). The implementation of MN-LAMR into EMT improves its system efficiency according to the smaller number RMSE in comparison between EMT and EMT-CG. The findings highlight the hybrid CG method's capability within the EMT system which will be useful in enhancing the medical field technology.
Copyright (c) 2025 Michelle Ubong Sari, Dzaihan Dzul, Nurfatihah Anizan, Nur Syarafina Mohamed, Norhaslinda Zull Pakkal https://creativecommons.org/licenses/by-nc-sa/4.0
Solution of Monkeypox Transmission Model using Picard Iterative Method
Monkeypox, an emerging zoonotic disease, has shown potential for human-to-human transmission, which warrants detailed study of its spread dynamics. Understanding the dynamics of disease transmission is essential for effective control and prevention strategies. Mathematical models play a key role in studying these dynamics, but the reliability of the numerical methods used to solve these models can vary. Traditional approaches may face challenges in terms of accuracy and computational efficiency. In this paper, the Picard Iterative Method (PIM) is applied to Monkeypox Transmission model for fatal disease, which is an eight-dimensional system of nonlinear ordinary differential equations. This study introduces a novel approach by applying the Picard Iterative Method to solve the monkeypox transmission model. Previous studies have primarily focused on using both integer-order and fractional-order mathematical models to simulate monkeypox transmission dynamics. By employing the Picard Iterative Method, this research provides a fresh perspective and contributes to the development of numerical techniques in infectious disease modeling. Comparison between the Picard iterative solution and the classical Runge-Kutta (RK4) numerical solutions are made. Findings indicate that the Picard Iterative Method is reliable in short-term predictions of the monkeypox model, offering insights into potential outbreak patterns and informing public health responses.
Copyright (c) 2025 Ahmad Bazli Bin Khairuddin, Mat Salim Bin Selamat, Nor Azizah Binti M. Yacob https://creativecommons.org/licenses/by-nc-sa/4.0
Online Social Sentiment Analysis: Discerning Customer Satisfaction via Twitter Reviews on Malaysia’s Courier Service
As e-commerce continues to grow, customers frequently encounter issues with courier services such as late deliveries, tracking problems, and unsatisfactory customer support. Although courier services are widely used, there is no dedicated platform that helps users choose the best provider based on social media sentiment. This study investigates customer satisfaction with courier services in Malaysia using Twitter reviews, focusing on three prominent providers: PosLaju, GDex, and DHL. A web-based application was developed using the Flask framework in Python and deployed on the Heroku platform. Sentiment analysis was performed using the TextBlob library, which classifies tweets into positive, negative, or neutral categories. TextBlob was selected for its ease of use and effectiveness in handling basic natural language processing tasks such as polarity detection and phrase extraction. The sentiment results were visualized to reveal trends in user feedback. The analysis showed that PosLaju had the lowest positive sentiment, with only 4.1 percent, and a relatively high negative sentiment of 14.9 percent. DHL received the highest positive sentiment at 39.6 percent and 20.8 percent negative, indicating a stronger approval among users. GDex showed moderate levels of both positive and negative feedback. This study provides valuable insight into customer perceptions of major courier services in Malaysia. By highlighting sentiment trends based on real user experiences from Twitter, the platform can assist future customers in selecting the courier service that best matches their needs.
Copyright (c) 2025 Nur Azmina Mohamad Zamani, Ahmad Muhyiddin Yusof, Nurul Zawani Muhamad Shukri https://creativecommons.org/licenses/by-nc-sa/4.0
Comparing Stochastic Models for Forecasting Mortality in Malaysia: Lee-Carter, Booth-Maindonald-Smith, and Age-Period-Cohort
Problem: The Human Life Table Database (HLD) provided the death rate data utilised in this study. With an emphasis on gender disparities and applying data from 2001 to 2020, this study analyses the problem of selecting the most accurate stochastic model for forecasting mortality rates in Malaysia. Aims/Objectives: This study compares the Lee-Carter, Booth-Maindonald-Smith, and Age-Period-Cohort models to determine which stochastic model is more effective in predicting gender-specific death rates in Malaysia between 2021 and 2026. Methodology/approach: The Human Life Table Database (HLD) included mortality rates for male and female participants aged 0 to 80. The data were divided into 5-year age intervals from 2001 to 2020. The Lee-Carter model, the Booth-Maindonald-Smith model, and the Age-Period-Cohort variant of the Lee-Carter model are the three stochastic models that the study applies to this dataset. The residuals and error data were analysed to determine how well each model predicted mortality rates. The model producing the lowest error values was determined to be the most suited model for forecasting mortality trends. Comparisons were conducted across the models to assess the accuracy and robustness of the models in representing the mortality dynamics for both genders in Malaysia. Results/finding: The Lee-Carter model was identified as the most accurate stochastic model for forecasting mortality rates in Malaysia from 2001 to 2020, as it produced the lowest error values compared to the other models.
Nurul Akmar Binti Mohamad Fadzill Mohamad Fadzill
Copyright (c) 2025 SHAMSHIMAH SAMSUDDIN, Natasya Mazlan, Nur Aqilah Nadiera Nazhar, Nurul Akmar Mohamad Fadzill , Nurul Izzah Mohamad Naser https://creativecommons.org/licenses/by-nc-sa/4.0
Assessing Flood Risk through Frequency Analysis at Sayong River Station
This research investigates extreme flood events at Sayong River Station using two statistical distributions: the Generalized Logistic (GLO) and Generalized Extreme Value (GEV) distributions. The study employs the L-moment method for parameter estimation and evaluates quantile estimates for return periods of 10, 50, and 100 years. The primary objective is to determine which distribution provides a more accurate representation of extreme flood behavior. Quantile estimates derived from the GLO distribution are 197.3153 m³/s for the 10-year return period, 363.8308 m³/s for the 50-year return period, and 469.9711 m³/s for the 100-year return period show better alignment with observed data compared to the GEV distribution. The GLO distribution's superior performance indicates its enhanced ability to capture the tail behavior of extreme floods, providing more reliable estimates for flood risk management. The findings emphasize the importance of selecting an appropriate distribution model for accurate flood risk assessment. The efficacy of the GLO distribution in representing extreme values underscores its appropriateness for forecasting extreme flood magnitudes and providing guidance for infrastructure design and flood mitigation measures.
Copyright (c) 2024 Nur Diana Zamani, BASRI BADYALINA, Muhammad Zulqarnain Hakim Bin Abd Jalal, Rusnani Mohamad Khalid, Fatin Farazh Ya’acob, Kerk Lee Chang https://creativecommons.org/licenses/by-nc-sa/4.0
E-Learning Application based on Gagne’s Instructional Design Model (E-DBLEARN)
Learners often struggle with the complex and interconnected terminology in database environments and data modeling, leading to difficulties in understanding and applying key concepts effectively. In addition, a preliminary study has revealed that inadequate classroom facilities, varying learning styles, and the use of external resources that are not tailored to the syllabus have negatively impacted the understanding of theory-based topics. This study aims to achieve several objectives: identifying requirements of an e-learning application for Introduction to Database, design and develop the e-learning application and evaluating the e-learning application’s functionality and usability. This research has been initiated by preliminary analysis on the application requirements based on literature review and surveys. The design phase followed, producing essential diagrams which are navigation map and storyboards. The development was done utilizing Adobe Animate, Adobe Premiere Pro, Adobe Audition and Adobe Photoshop for producing the application for learning database. Lastly, the functionality and usability of the application was verified through testing. Usability testing involved expert evaluation with input from three experts. The results acquired give a positive indicator for further implementation. Further evaluation with 30 respondents from the target users has produced results indicated high satisfaction with the application’s overall usability, particularly in visual design and self-assessment and learnability constructs, which achieved the highest mean scores of 4.80 with standard deviations of 0.41 and 0.48, respectively. In conclusion, E-DBLEARN offers university students studying database courses an interactive, multimedia-rich platform expected to boost engagement, understanding, and self-directed learning, while equipping lecturers with an effective tool to teach theoretical concepts in the database courses. Gagne’s Instructional Design Model was incorporated to ensure that learning is effective, efficient, and structured by guiding the instructional process through a systematic approach.
Copyright (c) 2024 Nor Azila Awang Abu Bakar, Fatin Najwa Kamal, Siti Nurul Hayatie Ishak, Norlina Mohd Sabri https://creativecommons.org/licenses/by-nc-sa/4.0
Application Of Cubic Trigonometric Hermite Interpolation curve With Shape Parameters In 2 Dimensional Objects
Problem: Achieving accurate mathematical representations of an object's geometry for computer-aided design and manufacturing is complex. Designing both objects demands a method that provides the necessary flexibility and smoothness in curves, which traditional methods lack of flexibility and smoothness in curves needed for precise and efficient design. Aims/Objectives: The main objective is to explore and implement cubic trigonometric Hermite interpolation as a method for constructing two-dimensional objects. Next, to discussed the properties of this interpolation method and to determine the best value of the shape parameter that produces smooth curves Methodology/approach: Cubic trigonometric Hermite interpolation is used for constructing two-dimensional objects. This process is conducted using Mathematica. The different values of the shape parameter are used to achieve the desired curve characteristics in the geometric models. Results/finding: The correct images of the two dimensional objects are also located and the suitable shape parameters can be obtained after studying the behavior of the curve using free parameters. Two dimensional object can be formed using cubic trigonometric Hermite interpolation without making any easy blunders or errors. Implication/impact: The impact of this project lies in its potential to improve geometric modelling by providing the best value of shape parameter that enhances the precision and efficiency of creating two-dimensional geometric models. This can significantly benefit technical drawing, computer-aided design, and manufacturing by offering a more flexible and accurate way to represent objects' geometry.
Copyright (c) 2024 NURSYAZNI MOHAMAD SUKRI, FARAH NABILAH AZMI, NOOR KHAIRIAH RAZALI , SITI MUSLIHA NOR-AL-DIN https://creativecommons.org/licenses/by-nc-sa/4.0
COMPARATIVE ANALYSIS OF EULER AND RUNGE-KUTTA FEHLBERG METHODS IN SOLVING THE LOTKA-VOLTERRA COMPETITIVE MODEL
Problem: Based on articles written by Razali and Abdullah (2013), exclusion often occurs at a slow rate. Therefore, it was difficult to determine if equilibrium and stability exist. Equilibrium is the state of an ecosystem in which all components are in balance with one another. In addition, equilibrium species supposedly survive through a competitive survival strategy rather than through reproduction or dispersal to survive in harsh conditions where food resources are scarce. Unfortunately, based on the principle of competitive exclusion proposed by Gause (1934) which states that two species competing for the same resources cannot coexist, one will drive out. Aims/Objectives: 1) To analyse the competitive interactions between lions (Panthera leo) and leopards (Panthera pardus) in the Sabi Sand Game Reserve, South Africa by comparing among exact solutions using Euler and RKF methods on the Lotka–Volterra Competitive model. 2) To discuss the impact of carrying capacity (capability survival) of dynamic behaviors in competition between two different species. 3) To analyze equilibrium and stability of the competition between two species depending on the initial conditions. Methodology/approach: This study used data from Balme et al. (2017) to examine competitive interactions between two top predators, lions (Panthera leo) and leopards (Panthera pardus) from 2010 to 2015 in the Sabi Sand Game Reserve, South Africa. This study will test whether lions, as the dominant competitor, will limit the distribution and abundance of leopards by using dietary data collected concurrently on the 2 species. Dietary overlap between lions and leopards was initially limited, with lions targeting large- to very large-sized prey and leopards small- to medium-sized prey. The Euler and Runge-Kutta Fehlberg (RKF) methods will be applied for solving this model and subsequently compare the simulation results with their exact solutions. It is anticipated that these numerical approximations will undertake to find out approximate solutions to nonlinear problems. Numerical approximation tests the reliability and precision of both methods applied to the logistic equation. This result is supported by computations carried out using the Mathematica 13.2 software. Results/finding: The findings indicate that the RKF method provides an accurate approximation, outperforming the Euler method. The results demonstrate that a larger carrying capacity is associated with a greater ability for a species to thrive and survive in competitive environments. Carrying capacity refers to the maximum population size a species can sustain indefinitely, considering factors like food, habitat, water, and others. During the curve-fitting procedure, it was observed that leopards have a higher carrying capacity compared to lions. Therefore, it can be inferred that the population of leopards is not significantly affected by the presence of lions. Implication/impact: The study will be beneficial to future researchers who engage with nonlinear differential equations, mathematical biology, and numerical techniques. By utilizing data stimulation to mimic real-world problems. As suggestion, this study can provide insights and solutions for related issues faced by the Department of Wildlife and National Malaysia. For example, it can aid in the restoration of large carnivores to support struggling ecosystems in Malaysia (Ripple, 2014). Furthermore, this study will contribute to the verification of the theory of exclusion and its applicability in various competitive scenarios. It will help determine whether both predator species, lions, and leopards, can coexist while undergoing competition. Additionally, the study will demonstrate the practical application of numerical approximation as a predictive tool, enabling researchers to determine the outcome of interspecific competition without the need for extended experimental periods. Moreover, these findings will enhance our understanding and provide illustrations of how numerical approximation can be utilized to predict competition outcomes, aiding researchers in determining which species may prevail in different interspecific competition scenarios.
Copyright (c) 2024 NURIZATUL SYARFINAS AHMAD BAKHTIAR, Nur Hidayah Abd Rahaman, Hafizah Hajimia, Nur Fatihah Fauzi, Nur Izzati Khairudin https://creativecommons.org/licenses/by-nc-sa/4.0
Reliability and Construct Validity Assessment of the Student Competency Questionnaire among Final Year Diploma Students: A Statistical Analysis Approach
The university strives to provide students a curriculum that is pertinent and taught by educators with exceptional delivery methods, in a stimulating and dynamic learning setting. The objective is for students to graduate from the institution as individuals who can positively impact society, establish businesses, and assume leadership roles in the professional realm. The objectives of this research are to develop the Student Competency Questionnaire (SCQ) and evaluate its reliability and validity. The evaluation followed classical test theory, focusing on reliability, construct validity, and content validity. A postal questionnaire containing 49 items was sent in October 2022 to a sample of 59 final-year diploma students from various programs. Three experts in education, statistical modeling, and decision-making evaluated the content validity of the scale. The Content Validity Index (CVI) for all constructs ranged from 0.96 to 1, exceeding the threshold of 0.70, indicating that the items are 'content valid.' Cronbach's alpha values showed a high level of internal consistency, all exceeding 0.90. Additionally, all items within each construct were highly related to one another, with correlations exceeding 0.30. This research demonstrates that the Pillar 1 education 5.0 @ UiTM frameworks effectively capture essential skills for students' academic and professional development, enhancing their employability. The focus on Personal, Adaptive, Digital, 21st Century, and Social competencies, along with student satisfaction, ensures students are equipped with diverse skills needed to thrive in dynamic environments. Overall, UiTM's approach prepares students for success in their future careers and personal lives.
Copyright (c) 2024 Nor Faezah Mohamad Razi, Dr.Norhayati https://creativecommons.org/licenses/by-nc-sa/4.0
An Evaluation of Online Food Delivery Application: Fuzzy Analytical Hierarchy Process
Food delivery is one of the businesses reporting growth rates after the COVID-19 pandemic. Due to social isolation and extensive lockdowns worldwide, individuals who once dined out now choose online food delivery. To keep up with the ever-changing market dynamics, the service provider must be aware of various measurements and aspects related to sustainable growth. Therefore, this study aims to determine the criteria for evaluating online food delivery applications, calculate the weight for criteria, and rank the online food delivery applications according to the most preferred by the customers. This study uses a multi-criteria decision-making (MCDM)-based framework which is the Fuzzy Analytic Hierarchy Process (FAHP). The FAHP is used to produce weights for criteria by applying fuzzy set theory to the linguistic evaluation statements of experts and ranking the online food delivery applications according to the customer's preference. The findings indicate that Foodpanda is the most preferred food delivery application, followed by GrabFood and McDelivery. The most crucial main criterion is economics, with delivery cost as the priority sub-criteria. The second most important criterion is technology, the third is service quality, and the last is social and environmental. This study is useful for the service provider in improving the criteria that will most affect the customer, as well as for the customer to wisely choose the e-service application that meets their demand.
Copyright (c) 2024 Nur Solihah Khadhiah Abdullah https://creativecommons.org/licenses/by-nc-sa/4.0
Complex Network Analysis on NASDAQ Stock Market around COVID-19
Problem: The United States plays an important role in the global economy, and the COVID-19 pandemic significantly affected the U.S. stock market. Over the two decades, numerous studies have incorporated complex network analysis to analyze the stock market, but there is a lack of study in identifying anomalies in network structures during financial crises. Aims/Objectives: The main objective of this research is to implement complex network analysis in examining the changes in the network structures and centralities of the NASDAQ stock networks at periods around the COVID-19 pandemic. Methodology/approach: The opening prices of the stocks under NASDAQ index in the last two quartile of 2019 and the first quartile of 2020 were collected from Yahoo Finance. The collected data was parsed into edges lists which was then used to construct multiple stock networks. The structures of the stock networks were analyzed using topological metrics such as network density, average clustering coefficient, average path length, network centralizations, and modularity of community structure. The centrality scores of the stocks in the networks were calculated and they were ranked according to the scores. Results/finding: The results show abnormal values in the number of edges, network density, betweenness centralization, and modularity of the community structure in the initial stages of the COVID-19 pandemic. However, no significant anomalies are observed in the average clustering coefficient, average path length, degree centralization, and closeness centralization. Meanwhile, degree centrality proves effective in identifying influential stocks, while closeness and betweenness centralities are found to be less suitable for this particular purpose in the networks used in this study. Implication/impact: This paper provides insights on the changes within the stock market at both micro and macro levels around financial crisis, where the anomalies serve as indicators of an impending financial crisis.
Copyright (c) 2024 Jia Hou Chin, Nor Aziyatul Izni https://creativecommons.org/licenses/by-nc-sa/4.0
Predicting Default and Non-Default Firms using Discriminant Analysis: Adaptation of KMV-Merton's Default Probabilities and Financial Ratios
The KMV-Merton model provides conceptual determinants for predicting firms' default risk, but its accuracy was tested long ago, and it contains insufficient statistics for default prediction. Therefore, previous literature adapted the KMV-Merton model into a statistical model involving financial ratios to improve its predictive capabilities. Discriminant Analysis (DA) is a widely used statistical model for predicting financial distress. The objectives of this study are to identify financial ratios significant to KMV-Merton's default probabilities using DA, to predict default and non-default firms using the DA model obtained, and to compare the performance of the KMV-Merton and DA models in predicting default risk. The study uses 11 years of data from Malaysian publicly listed firms, applying the KMV-Merton model and stepwise DA in SPSS. DA identifies the significance of selected financial ratios to firm default, with KMV-Merton's default probabilities as the dependent variable, forming a discrimination function to predict default and non-default firms. Credit ratings and Type 1 and Type II errors are used to compare model performance. The DA using SPSS reveals a discriminant function with net profit margin and return on assets significantly related to KMV-Merton's default probabilities. The DA model is more biased in predicting non-default firms due to the need for more information on default firms, yet it slightly outperforms the KMV-Merton model. This study offers guidance on adapting KMV-Merton's default probability estimates with financial ratios in the DA model and highlights the significant financial ratios related to KMV-Merton's default probabilities.
Copyright (c) 2024 Norliza Muhamad Yusof, Nur Ain Al-Hameefatul Jamaliyatul, Nurul Afiqah Zainuddin, Izza Suraya Zulhazmi, Muhamad Luqman Sapini https://creativecommons.org/licenses/by-nc-sa/4.0
Determinants of Smartphone Prices using Backward Elimination Technique in Multiple Linear Regression
Problem: The rapidly evolving market for smart gadgets causes smartphone prices to vary widely, frequently posing challenges to what consumers expect and can afford. Therefore, understanding the complex interrelationship of factors that determine smartphone prices has emerged as an important subject for study in an era defined by technological developments. Aims/Objectives: This study seeks to identify the factors that influence the pricing of smartphones. Methodology/approach: The study focused on various factors, including the battery capacity, camera quality, screen size, charging speed, device weight, and age in months. The primary data for the research came from the Global System for Mobile Communication (GSM) online marketplace, comprising sixty smartphones selected through a simple random sampling technique. We initially developed a multiple linear regression model with SPSS and then refined it using backward elimination. Results/finding: The results highlight the strong influence of several characteristics on smartphone pricing, namely battery capacity, charging speed, weight, and model age. Interestingly, the examination of six variables revealed that camera and screen size had no effect on price. Implication/impact: The knowledge acquired from this quantitative analysis not only advances our comprehension of the interplay between technology and consumer demand but also has implications for manufacturers, policymakers, and consumers who desire to navigate the ever-changing sphere of smartphone pricing.
Copyright (c) 2024 Sarah Yusoff, Muhammad Hazwan Mohd Hazhar, Dzul Dzaihan Dzul Dzailani, Nuralya Sofea Hairulanuar, Nurfatihah Anizan https://creativecommons.org/licenses/by-nc-sa/4.0
Efficiency Of Malaysian Life Insurance Company Using Two-Stage DEA Method
The insurance industry provides unique financial services for the growth and development of every country. The efficiency measurement in the insurance companies may increase the quality of their operations and assist in identifying the inefficacy stages for future improvements. This research intends to evaluate the efficiency of 12 life insurance companies in Malaysia by applying the Traditional DEA method as well as the Two-Stage DEA method in the year 2021. Traditional DEA views the actions of decision-makers as a black box, overlooking the intermediate process which is unsuitable for network systems in the insurance industry. The production process of a life insurance company is divided into two sub-processes, which are operational and profitability stages, suitable to be evaluated by the Two-Stage DEA method. This method calculates the efficiency score of the DMUs by equally considering the intermediate phases. The model was executed using Lingo 20.0 software yielding efficiency scores ranging from 0 to 1. The findings revealed that five companies performed efficiently throughout the whole production process under Traditional DEA. However, none of these five companies has an overall efficiency score of 1 under the Two-Stage DEA. This is because none of those companies are efficient in both sub-processes. This reveals the limitations of Traditional DEA thus offers valuable insights to Malaysian life insurance companies on the effectiveness of Two-Stage DEA in assessing operational and profitability efficiencies. This study aids companies identify areas for improvement and encourage benchmarking against efficient competitors.
Copyright (c) 2024 Nur Rasyida Mohd Rashid, Nur Diyana Awanis Razaly, Nurul Nadia Saharuddin, Muhamad Faris Iskandar Mohamad Fauzi https://creativecommons.org/licenses/by-nc-sa/4.0
Total Variation-based Active Contour Model for Medical Images
The Active Contour Model (ACM) is a mathematical model in image processing that is commonly utilized to partition or segment an image into specific objects. The segmentation method in region-based ACM can be categorized into two classes: global ACM and selective ACM Selective ACM isolates a specific target item from an input image, which is more advantageous than the global ACM due to its proven use, particularly in medical image analysis. However, the selective ACM appears to produce poor outcomes when segmenting an image with uneven (inhomogeneous) intensity. Additionally, the current selective ACM that uses the Gaussian function as a regularizer generates a non-smooth segmentation curve, especially for images containing noise. This study introduces a new selective ACM that is designed to segment medical images with inhomogeneous intensity levels. The model incorporates a Total Variation term as a regularizer, distance function, and local image fitting concepts. The Euler-Lagrange (EL) equation was given to solve the suggested model, which is approximately 5% more accurate with a processing time that is around three times faster than the existing model, as shown by numerical testing. The suggested mathematical model can be advantageous for the image analysis community, particularly in the medical industry, to automatically segment a specific object in a medical image.
Copyright (c) 2024 ABDUL KADIR JUMAAT https://creativecommons.org/licenses/by-nc-sa/4.0
Optimizing Procurement System Using Nielsen’s Heuristics
A fitness center grapples with significant challenges related to administrative errors, prolonged purchase cycles, and supplier management issues, resulting in escalated costs and operational inefficiencies. To address these hurdles, this project aims to develop a procurement system for this fitness center, targeting the optimization of procurement activities, improvement of inventory management, and facilitation of smoother interactions with suppliers for managers, staff, and suppliers alike. The objective of this study was to identify the requirements for the procurement system, design and develop the system with integrating Nielsen's heuristics. Furthermore, system evaluation has been conducted to test its functionality and usability to the target users. Utilizing an adapted waterfall model as the framework, the system's scope encompasses user registration, login procedures, purchase order submission, invoice generation, and equipment maintenance and management. The evaluation results demonstrated that the system meets all requirements outlined in the test plan. Additionally, feedbacks from both users and experts have been integrated to further refine the system. Implementation of this system will make the procurement process smoother and more organized. By prioritizing usability, efficiency, and user satisfaction, the system has the potential to optimize procurement activities, reduce errors, and ultimately improve the overall functioning of the fitness center.
Copyright (c) 2024 SITI NURUL HAYATIE ISHAK https://creativecommons.org/licenses/by-nc-sa/4.0