Journal of Applied Science, Engineering, Technology, and Education

Artificial Intelligence Readiness in Malaysian Libraries: An Assessment Framework

Mohamad Noorman Masrek — 2025-08-31

This study investigates the readiness of Malaysian libraries to embrace Artificial Intelligence (AI) technologies, focusing on six key dimensions: infrastructure, technical skills, management support, policy and regulations, funding, and training. Utilizing a quantitative research approach, an online survey was conducted to collect data from library heads, providing insights into their preparedness for AI adoption. The survey instrument, developed based on preliminary interviews with librarians and refined through expert feedback and pilot testing, achieved good internal consistency. Findings reveal that while libraries exhibit moderate readiness in terms of internet infrastructure and management support, significant challenges remain in financial planning and policy and regulation formulation for AI integration. These challenges highlight critical areas that need addressing for successful AI adoption. The study provides valuable insights for library administrators and policymakers, emphasizing the need for comprehensive strategies to enhance AI readiness. The contributions of this study are threefold. Theoretically, it expands the understanding of AI readiness in the library sector, offering a validated framework for future research. Empirically, it fills a gap by providing data-driven insights into the current state of AI readiness among Malaysian libraries. Practically, it offers actionable recommendations to improve financial planning and policy development, essential for effective AI integration.

Modified SIFT-Based Kirsch Edge Detection Approach for Copy-Move Forgery Detection

Bashir Idris — 2025-08-31

The increasing accessibility of digital imaging technology has led to a rise in image forgery, raising concerns about digital content authenticity in forensic and security domains. Copy-move forgery is one the most prevalent and challenging forgery techniques due to its seamless manipulation. We propose a novel passive CMFD (CMFD) approach that leverages a modified Kirsch (mKirsch) edge detector and a modified SIFT-based descriptor (DivSIFT) to accurately identify and localize copy-move forgery (CMF). The mKirsch edge detector enhances edge detection by selectively deleting specific masks, improving keypoint extraction and feature matching. We used MICC-F220, CoMoFoD, and MICC-F8Multi datasets to measure the performance of the new method, under challenging conditions such as rotation, scaling, JPEG compression, and multiple forgeries. The results show that mKirsch-enhanced detection outperforms compared to conventional Kirsch-based methods. Notably, methods with deleted masks (WW_NW and NE_SE) achieved a True Positive Rate (TPR) of 90.91%, precision 100%, and an F-measure of 95.24%. Robust against rotation and scaling attacks, achieving a TPR of up to 96.97% with zero false positives. Additionally, the method is computationally efficient, with an execution time of 2.74 seconds, making it suitable for real-world applications. These findings establish the mKirsch-based CMFD as a highly accurate and efficient solution for image forgery detection in digital images.

Programming Evolution through Computational Thinking Using The Bibliometrics Analysis

Nurbaya Mohd Rosli — 2025-08-31

The increasing significance of programming in various fields has made understanding its evolution a crucial academic pursuit. Despite the growing importance of programming, there is a lack of comprehensive analysis that integrates programming evolution with the nuances of computational thinking. This study explores an in-depth examination of the developmental trajectory of programming, contextualized within the broader framework of computational thinking. The aim of the paper is to decode the patterns, trends, and shifts in programming paradigms, tools, and education, contributing to the academic discourse on the subject. Using bibliometric analysis, the study examines a broad array of academic publications and data from the past decade. Advanced data mining in Scopus database and VOSviewer 1.6.20 are employed to trace the progression of programming concepts and their educational implications. Findings indicate a major transition from traditional paradigms to more inclusive, intuitive approaches that emphasize real-world problem-solving and interdisciplinary applications. The analysis reveals a significant shift from traditional programming paradigms towards more inclusive and intuitive approaches, emphasizing real-world problem-solving and interdisciplinary applications. While, educational trends show a gradual integration of computational thinking into curricula, reflecting the need to equip learners with relevant programming skills.

Downside Risk Measurement of Indonesian Financial and Energy Securities: A Value at Risk Perspective

Zahara Tussoleha Rony — 2025-08-31

This study explores the measurement of downside risk in the Indonesian capital market by applying Value at Risk (VaR) analysis to two representative companies from different economic sectors: PT Asuransi Bina Dana Arta Tbk (ABDA), an insurance firm, and PT ABM Investama Tbk (ABMI), an energy and mining company. Utilizing daily return data from December 2023 to November 2024, we assess both individual and portfolio-level risk profiles through a quantitative framework based on Monte Carlo simulation. Our findings reveal distinct risk-return characteristics between the two stocks. ABDA exhibits a negative average daily return (-0.15%) with relatively low volatility (standard deviation of 1.12%), whereas ABMI shows a positive expected return (0.07%) but higher variability (standard deviation of 2.06%). Interestingly, despite its lower volatility, ABDA records higher VaR values across all confidence levels, indicating greater downside exposure—particularly evident at the 95% confidence level where ABDA's one-day VaR is -2.04%, compared to ABMI’s -1.77%. A cross-sector portfolio consisting of 40% ABDA and 60% ABMI demonstrates meaningful diversification benefits, reducing the standard deviation to 1.02% and improving the VaR to -1.54%. This represents a risk reduction of 24.5% compared to ABDA alone and 13% relative to ABMI alone. For a hypothetical investment of IDR 100 million, these improvements equate to reduced potential daily losses of IDR 508,258 and IDR 233,504 respectively. The results emphasize that strategic cross-sector allocation—even within a simple two-asset portfolio—can significantly enhance risk-adjusted performance in emerging markets like Indonesia. By combining financial and energy assets with differing sensitivities to macroeconomic conditions, investors can better manage downside risk while maintaining exposure to diverse economic drivers. This research provides a practical methodology for risk assessment and portfolio construction, offering valuable insights for asset managers and decision-makers operating in volatile and multi-sector investment environments.

PLS-SEM Approach: Validity and Reliability of a Questionnaire on Context, Input, Process and Acceptance of STEM Implementation in Malaysia

Mohammad Azri Bin Amatan — 2025-08-31

This study aims to test the validity and reliability of the context, input, process, and acceptance of STEM implementation questionnaires for secondary school teachers in Malaysia. Partial Least Squares Structural Equation Modelling (PLS-SEM) with SMARTPLS 3.2.3 software was used to do the analysis. The study involved 825 secondary school teachers using stratified random sampling. Based on composite reliability and Cronbach's alpha values, the Reflective Measurement Model showed that there was internal consistency reliability. Convergent validity was also achieved through outer loading analysis and average variance extracted (AVE). It also met the criteria for discriminant validity based on the Fornell-Larcker Criterion, Cross Loading, and the Heterotrait-Monotrait (HTMT) Ratio. The findings of this study demonstrate that this questionnaire is valid and reliable for assessing STEM implementation among secondary school teachers in Malaysia.

Engineering-Based Investment Modeling in Emerging Markets: Decision Analytics for Portfolio Management in Indonesia

Ayu Puspitaningtyas — 2025-08-31

This study presents an engineering-based approach to investment modeling in the context of emerging markets, with a focus on portfolio management strategies in the Indonesian equity market. By integrating computational finance techniques and decision analytics, the research develops a structured framework for assessing risk-return dynamics and optimizing asset allocation decisions. Using daily stock return data from two representative companies across different economic sectors—PT Telekomunikasi Indonesia Tbk (TLKM.JK) from telecommunications and PT Petrosea Tbk (PTRO.JK) from energy/mining—the study applies Monte Carlo simulation, Value at Risk (VaR), and statistical decision modeling to evaluate both individual and combined investment performance. Results indicate distinct risk-return profiles, where PTRO.JK offers higher average returns but with significantly greater volatility compared to TLKM.JK. A cross-sector portfolio consisting of 60% TLKM.JK and 40% PTRO.JK demonstrates notable diversification benefits, reducing overall portfolio risk as evidenced by improved VaR estimates. For a hypothetical IDR 100 million investment, the diversified portfolio reduces maximum expected loss by more than 50% compared to investing solely in TLKM.JK. These findings highlight the effectiveness of quantitative, model-driven approaches in supporting strategic investment decisions. By bridging financial engineering methodologies with practical portfolio management needs in Indonesia, this study contributes a replicable framework that enhances decision-making under uncertainty, particularly for investors seeking to balance growth potential with downside risk control in multi-sector allocations.

The Rasch Model Analysis of Secondary School Teachers' Acceptance of STEM Implementation in Teaching and Learning in Labuan

Mohammad Azri Bin Amatan — 2025-08-31

This study aims to assess the acceptance of the implementation of STEM approaches in teaching and learning by secondary school teachers in the Federal Territory of Labuan. This study uses a quantitative assessment approach of a sample survey type. Data were collected through the STEM Implementation Acceptance Questionnaire involving 314 teachers who were randomly selected from nine secondary schools. Rasch model data analysis using WINSTEPS Version 3.69.1 software was applied to produce a Wright Map to measure the acceptance aspects of STEM implementation through the study questionnaire items. The results of the study showed that items that were difficult to accept included making quantitative predictions (measure > 1 logit), reducing exam stress, and developing a problem-solving model, while items that were easy to accept were fostering positive values, early planning, and various teaching and learning methods (measure < -1 logit). The component of teacher belief in STEM recorded the highest mean measure value (0.43 logit), followed by attitude (-0.30 logit) and teacher commitment (-0.11 logit). Teachers were more likely to accept the aspects of attitude and commitment than belief in the importance of STEM. The findings of this study provide an overview of the strengths and weaknesses of teachers' acceptance of STEM implementation in teaching and learning, which should be taken into account to increase teachers' confidence, attitude, and commitment towards the implementation of STEM in secondary schools.

IoT-Based Real-Time Monitoring and Thermal Performance Evaluation of A Box-Type Solar Cooker

Badri N. Panigrahi — 2025-08-31

This work presents the development and evaluation of an IoT-based real-time monitoring system integrated with a box-type solar cooker to assess its thermal performance. The system incorporates an ESP8266 microcontroller interfaced with a DHT11 sensor for ambient temperature and humidity, a DS18B20 sensor for absorber plate temperature, and a MAX6675 with a K-type thermocouple for vessel (water) temperature measurement. All of the sensor data is transmitted to the ThingSpeak cloud platform, enabling real-time visualization and remote analysis. Thermal performance indicators were derived from the recorded data, including the first figure of merit (F₁), second figure of merit (F₂), thermal efficiency (η), and the overall heat loss coefficient (UL). Under clear-sky conditions, the cooker reached a stagnation temperature of approximately 130°C, with ambient conditions near 35°C and solar irradiance around 850 W/m². The calculated F1 (~0.11 °C•m²/W), F2 (~0.41), average thermal efficiency (~30 %), and average heat loss factor (~4.8 W/m²•°C) signify a reasonably efficient solar cooker design. Additionally, real-time insights into heat retention and loss were made possible through continuous monitoring. The integration of IoT with the solar cooker not only improves data acquisition and performance evaluation but also demonstrates the viability of smart sustainable cooking technologies in modern applications. It allows for easy and better evaluation of the cooker by the general user

Computational Risk Management for Strategic Investors: An Engineering-Inspired Approach to Portfolio Diversification

Nonce Hasan — 2025-08-31

This study presents an engineering-inspired approach to computational risk management, focusing on portfolio diversification strategies for strategic investors. Utilizing Monte Carlo simulation techniques, we assess the Value at Risk (VaR) of two prominent companies in Indonesia’s entertainment sector: MD Pictures Tbk (FILM) and MNC Studios International Tbk (MSIN). Daily return data from January 2022 to December 2022 are analyzed to evaluate both individual and combined risk-return profiles. The results show distinct characteristics between the two stocks, with FILM exhibiting a higher average daily return (0.42%) but greater volatility (standard deviation of 4.01%), compared to MSIN's more moderate return (0.33%) and lower volatility (3.64%). At a 95% confidence level, VaR estimates indicate potential maximum daily losses of 6.12% for FILM and 5.81% for MSIN. When combined into an equally weighted portfolio, significant diversification benefits emerge, reducing portfolio standard deviation to 2.77% and improving the VaR to 4.58%. This represents a risk reduction of 25.2% compared to FILM and 21.2% compared to MSIN. For a hypothetical investment of IDR 100 million, these improvements translate to reduced potential daily losses of IDR 1,540,482 and IDR 1,229,142 respectively. The findings demonstrate that even within a single industry, effective risk management can be achieved through strategic intra-sector diversification when constituent firms differ in business model and operational focus. This research bridges the gap between financial engineering and strategic portfolio management by offering a quantitative, simulation-based framework that supports informed decision-making for risk-conscious investors.

Citrus Peel Waste as an Electrolyte Solution for Energy Storage in Bio-Batteries

I Wayan Suriana — 2025-08-31

This study explores the potential of citrus peel waste as an electrolyte solution for energy storage in bio-batteries. This research offers an environmentally friendly solution to obtain clean and sustainable energy by utilizing abundant organic waste as raw material. An experimental method was conducted to analyze the ability of electrolyte solutions from citrus fruit peel waste to support bio-battery performance. The results showed that citrus peel waste has significant potential as an electrolyte solution with competitive capabilities in energy storage. The implications of this research include the development of more sustainable alternative energy technologies and the effective use of unused resources, which could be an essential step in achieving global sustainability goals.

Perceptions of Students in Relevance of Management Course in Engineering Disciplines: A Higher Education-Implications for a 21st-Century Curriculum

Alberto E. Lastimado Jr — 2025-08-31

This study addresses the critical need to understand how engineering students perceive the relevance of engineering management education in preparing them for these multifaceted roles. Specifically, this research evaluates the perspectives of undergraduate engineering students in the Philippines regarding the significance of engineering management in their respective disciplines, its role in skill development, and the challenges encountered. A mixed-methods approach was employed, gathering data from 396 undergraduate engineering students through surveys, brief written responses, and interviews. Findings indicate that a significant majority of students hold favorable impressions, with 86.8% believing the knowledge gained will be valuable and 84.5% affirming that the course develops essential analytical and critical thinking skills. Students generally recognize the importance of engineering management in fostering fundamental abilities crucial for their future careers. However, a notable challenge is the perceived workload, with 52.7% of students finding management-related coursework demanding. The research illustrates the importance of integrating well-designed engineering management education within engineering curricula. The findings highlight the importance of equipping future engineers with the managerial expertise and soft skills the complexities of the 21st-century workforce and inform strategies for curriculum enhancement.

Artificial Intelligence in Educational Measurement: A Bibliometric Review (1997 to 2024)

Nursohana Othman — 2025-08-31

Artificial Intelligence (AI) is increasingly influencing measurement practices across fields such as health sciences, diagnostics, and psychology by enhancing accuracy, efficiency, and personalization. In the educational domain, however, the application of AI in assessment remains relatively underdeveloped, despite its significant potential to support adaptive systems and individualized feedback. This bibliometric review analyzes 921 peer-reviewed articles published between 1997 and 2024 to examine how AI contributes to the evolution of educational measurement. Through citation, co-citation, and keyword co-occurrence analyses, the study identifies influential publications, maps the intellectual structure of the field, and explores emerging thematic directions. Results indicate a substantial growth in research output after 2019, driven by advances in machine learning, natural language processing, and big data analytics. Foundational contributions from health and computer sciences, such as deep learning methods and open-source tools like Scikit-learn, have significantly influenced educational technologies. Three core themes are identified: the technical foundations of AI, cross-disciplinary applications in cognitive and medical diagnostics, and ethical and policy challenges related to AI implementation in education. Global collaboration is prominent, with leading contributions from the United States and China and increasing participation from Malaysia, Pakistan, and Nigeria. The review highlights the interdisciplinary nature of AI in educational measurement and calls for responsible, scalable applications to support inclusive and personalized learning environments

A Bibliometric Analysis of Scientific Publications at Universiti Malaysia Sabah

Anath Rau Krishnan — 2025-08-31

This study presents a comprehensive bibliometric analysis of scientific publications associated with Universiti Malaysia Sabah (UMS) from 1994 to 2024, tracing the university’s research progression over three decades. Using data extracted from the Scopus database, the study analyzes 9,217 publications by document type, open access (OA) status, and author keywords across three distinct decades using tools like Power Query, Microsoft Excel 365, and VOSviewer version 1.6.20. Findings reveal a steady increase in scientific publication productivity, with journal articles dominating output and OA publishing experiencing a significant surge in recent years. The keyword analysis reveals a consistent emphasis on local issues, particularly those related to Sabah and oil palm, and also reflects the university’s increasing engagement with emerging global themes such as COVID-19, machine learning, and deep learning. This study offers the first comprehensive, cross-disciplinary bibliometric analysis of UMS publications, uniquely highlighting the university’s growing engagement with open access publishing. The findings offer valuable guidance for future policy decisions, particularly in enhancing publication funding, promoting balanced growth across disciplines, and advancing open science initiatives.

Strategic Decision Analysis for Investment Portfolios: Computational Risk Assessment in Transportation Asset Management

Devy Sofyanty — 2025-08-31

This study presents a strategic decision-making framework for portfolio investment by applying computational risk assessment methods to the transportation sector in Indonesia. Focusing on two key players—PT. Adi Sarana Tbk (ASSA) and PT. Blue Bird Tbk (BIRD)—the research evaluates their individual and combined risk-return profiles using daily stock return data from December 2023 to November 2024. A hybrid analytical approach is employed, integrating traditional financial metrics with advanced computational techniques such as Monte Carlo simulation and Value at Risk (VaR) modeling to support managerial decision-making. The analysis reveals contrasting performance patterns: BIRD exhibits marginally positive expected returns (0.04%) but higher downside risk exposure, whereas ASSA shows negative average returns (-0.06%) yet demonstrates lower volatility and reduced extreme loss potential. Portfolio optimization results demonstrate that a diversified allocation of 60% ASSA and 40% BIRD generates improved risk-adjusted returns, achieving a positive expected return while maintaining lower overall risk compared to the individual assets. By incorporating monetary VaR estimates, this study enhances the practical relevance of risk analytics for portfolio managers, particularly in navigating volatile emerging markets. The findings underscore the importance of strategic asset allocation, business model differentiation, and quantitative risk modeling in constructing resilient investment portfolios. This research contributes both methodologically and managerially by offering a robust, replicable framework for strategic decision analysis in transportation asset management and beyond.

Optimizing Project Management: Planning, Scheduling, and Cost Estimation Using Microsoft Project

Saja Hadi Raheem Al-Dhamad — 2025-08-31

Event or activity management is done with computer-based software built especially for planning and secluding, such Primavera, Microsoft Excel, and Microsoft Project. The MS project's scheduling and organization are the goals of this study. Software of this type is easier to use and more efficient than manual methods. Although this program is based on manual approaches, it is more accurate and takes less time. Scheduling should be created for this investigation. The project case study focuses on the planning and scheduling of residential construction. In this instance, a two-story, 200-square-meter residential building in Baghdad/Abi Ghraib, Iraq, was selected. A work schedule should be created in the Microsoft project from excavation to completion, alerting the crucial path. Every task and piece of material should have its resources allocated. The MS project should include a calculation of the material and labor costs. According to this study, the scheduling and Planning benefits should be clarified. This study aims to investigate and inform the appropriate planning and selection for project management using Microsoft Project. It will also inform the budget, material, time, and deadline.

Robust Principal Component Analysis in Multivariate Applications

Sharifah Sakinah Syed Abd. Mutalib — 2025-08-31

Principal component analysis (PCA) is one of the multivariate methods that aims to construct components, each of which contains a maximal amount of variation from the data unexplained by the other components. Classical PCA (CPCA) is based on the classical covariance, which is easily affected by outliers. Additionally, the outliers may distort the PCA result. Robust statistics is one of the methods to overcome the outliers. Therefore, in this study, an alternative technique for robust principal component analysis (RPCA) is presented. Index Set Equality (ISE) is used as a robust estimator to robustify the CPCA and hence produce robust PCA (RPCA). The Hawkins-Bradu Kass dataset is used to illustrate the robustness of RPCA towards outliers compared to CPCA. The score plot, diagnostic plot, and performance measures are used to evaluate and illustrate the robustness of the RPCA. From the plots and performance measures, RPCA successfully identifies all outliers and is unaffected by masking and swamping effects. However, CPCA can only detect 0.2857 outliers, has 0.7143 masking effects, but does not have swamping effects. This study shows that RPCA, by using ISE as the robust estimator is a promising approach.

Exhausted Minds, Passionate Hearts: The Silent Struggle of Academic Burnout

Fatimah Bibi Hamzah — 2025-08-31

Burnout, characterized by chronic physical, emotional, and mental exhaustion, has become an increasingly recognized issue in academia, particularly within higher education institutions where teaching, research, and administrative responsibilities often overlap. This study investigates the prevalence, causes, and impact of burnout among 729 academic staff members at Kolej Poly-Tech Mara (KPTM) Sdn. Bhd. Utilizing the Maslach Burnout Inventory (MBI) along with additional data on workload, institutional support, and individual coping strategies, the research provides an in-depth analysis of burnout within the institution. The findings identify excessive workload, administrative burdens, and inadequate support systems as the primary drivers of burnout. Additionally, the study highlights the adverse effects of burnout on both personal well-being and professional effectiveness, with many staff reporting moderate to high levels of emotional exhaustion and depersonalization. Based on these insights, the study proposes solutions such as more equitable workload distribution, enhanced wellness programs, and the establishment of a more supportive work environment. By implementing these strategies, KPTM can alleviate burnout, improve staff well-being, and enhance overall institutional performance.