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WORK ENGAGEMENT AS A MEDIATOR BETWEEN AUTONOMY SUPPORT AND PSYCHOLOGICAL WELL-BEING
No full textThis study explored the role of supervisor and colleague autonomy support in relation to psychological, social, and emotional well-being, using the components of work engagement—vigor, devotion, and absorption—as mediators. Grounded in the Job-Demands Resources (JD-R) Model, the research highlights the importance of job resources, such as peer-support, in managing job demands and enhancing employee well-being, particularly within the context of education during the pandemic. A total of 315 participants, including teachers, staff members, and administrators, completed scales assessing work self-efficacy, job engagement, and mental health. The study utilized a cross-sectional and predictive research design, conducting the survey at a single point in time to determine the direct relationships and mediating roles of autonomy support and work engagement in relation to well-being. Data was analyzed using mediation analysis and confirmatory factor analysis, with model fit indices indicating an excellent fit. The findings revealed that supervisor and colleague autonomy support significantly predicted well-being, with work engagement fully mediating these relationships. Consequently, the direct effects of autonomy support on well-being became non-significant when work engagement was accounted for, indicating full mediation. Based on these results, it is recommended that educational institutions promote a supportive social environment, provide growth opportunities, and respect employee autonomy. This study contributes to the literature by elucidating pathways through which supervisor and peer autonomy support influence psychological, emotional, and social well-being through work vigor, dedication, and commitment
Deep Learning Approach in Seismology: Enhancing Earthquake Forecasting using K-Means Clustering and LSTM Networks
No full textLocated in the subduction zone of four tectonic plates, the high occurrence of seismic events is a severe threat in Indonesia. Mitigating the adverse effects of such disasters is essential to forecast the likelihood of future earthquakes. Consequently, developing a robust method of forecasting future earthquakes is critical to facilitate prevention and mitigation efforts. A reliable earthquake prediction method is necessary to reduce the after-effects to the greatest extent possible. This study utilises historical seismic and proposes innovative data pre-processing methods using K-means clustering to build a Long Short-Term Memory (LSTM) model for earthquake forecasting to overcome high-disparity locations. Four LSTM layers are embedded with adjusted fine-tuned network hyperparameters to enhance forecasting accuracy. The results attain 0.379816, 0.616292, and 0.414586 for Mean Square Error (MSE), Root MSE, and Mean Absolute Error, respectively, providing significant insights into earthquake prediction. In addition, predicted seismic occurrences are plotted on a map to display their geographic location within the specified research region. This research provides significant value in facilitating the efficient distribution of resources, such as evacuating residents impacted by earthquakes or reinforcing buildings and infrastructure, for emergency responders and policymakers
Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification
No full textMicroarray-based cancer biomarker detection is one of the popular trends for cancer classification. Though existing approaches have given competing performance in terms of classification accuracy and reduced feature subsets, the classification of different cancer microarray datasets still requires improvements. Recently, the swarm-based hybrid algorithms have given significant performance in cancer classification. However, the efficiency of a swarm algorithm is dominated by certain factors such as fitness value, convergence, exploration, and exploitation capabilities. Thus, a swarm-based hybrid approach is proposed for cancer classification with a new variant of the Firefly Algorithm (FA) and Correlation-based Feature Selection (CFS) filter. The slow convergence issue in the FA is resolved by non-fixed size solutions termed as mutable size solutions and a composite position update function is designed for the mutable solutions. In addition, the local optima issue is overcome by the population reinitialisation method. The proposed algorithm, named the CFS-Mutable Composite Firefly Algorithm (CFS-MCFA), is evaluated based on two metrics, namely classification accuracy and genes subset size, using a Support Vector Machine (SVM) classifier. Results show that CFS-MCFA-SVM achieved 100% accuracy with only a few biomarkers for all four cancer microarray datasets, indicating the efficiency and the competing performance of the proposed algorithm in biomarker detection for microarray-based cancer classification. Apart from that, the proposed algorithm would also contribute to cancer-related issues upon verifying the relevancy of particular genes via technical analysis from a medical perspective and would be utilised in feature selection applications
Adoption Intentions Toward AI-based Clinical Decision Support Tools: A Tam Study on Hospital Pharmacists
No full textThe increasing prevalence of antibiotic resistance leads to an alarming challenge to global healthcare, necessitating the adoption of machine learning (ML)-driven clinical decision support systems (CDSS) to enhance antimicrobial stewardship. Despite its potential, hospital pharmacist’s adoption of ML-based antibiotic resistance predictors remains limited due to usability, trust, organisational support, and perceived risk concerns. This study uses an extended version of Technology Acceptance Model (TAM) to examine the primary factors influencing pharmacists’ behavioural intention (BI) to adopt the ML-driven CDSS. A cross-sectional survey was conducted among 235 hospital pharmacists across major provinces in Indonesia, using a PLS-SEM approach to test hypothesised relationships. The findings reveal that perceived usefulness (PU) (β = 0.355, p < 0.001) is the strongest predictor of BI, followed by trust in technology (TT) (β = 0.179, p = 0.017), social influence (SI) (β = 0.184, p = 0.009), and facilitating conditions (FC) (β = 0.150, p = 0.010). Perceived risk (PR) negatively affects BI (β = -0.103, p = 0.023), highlighting concerns over AI reliability. The study underlines the need for hospital administrators to enhance IT support and training, policymakers to establish AI regulatory frameworks, and professional organisations to promote AI acceptance through peer advocacy. Clinically, increased adoption of ML-driven CDSS can improve the accuracy of antibiotic prescribing, reduce resistance rates, and enhance patient safety. It provides insights into optimising AI interventions in antimicrobial stewardship programmes and guiding future implementation strategies in hospital pharmacy practice
Enhancing the Effectiveness of Machine Learning-Based Phishing Email Detection via an Improved Pre-Processing Technique for Data Security
No full textThe growing volume and sophistication of phishing emails have become a significant threat to data security, often serving as the initial vector for data breaches. Most past studies have focused on comparing machine learning models to determine the best-performing algorithm. They often neglect the role of pre-processing, which also contributes to the effectiveness of these models. To address this gap, this study investigates the impact of pre-processing techniques on phishing email detection, aiming to strengthen data protection. Three supervised machine learning algorithms, which are Support Vector Machine (SVM), Random Forest and Decision Tree, were selected to undergo two experimental iterations: one with basic pre-processing and the other with an enhanced pre-processing technique including Synthetic Minority Oversampling Technique (SMOTE), Term Frequency-Inverse Document Frequency (TF-IDF), Singular Value Decomposition (SVD) and cross-validation. Using a dataset comprising 28,747 labelled emails, the models were trained, tested, and evaluated based on accuracy, precision, recall, and F1-score, with further insight gained through confusion matrix analysis. Among the models, Random Forest demonstrated the strongest consistent performance across all metrics, while Decision Tree showed the most notable improvement. Although SVM maintains high recall and precision, it is less responsive to the applied pre-processing techniques. This result demonstrates that pre-processing techniques significantly contribute to the performance of the detection models. Overall, these findings highlight the critical role of pre-processing in enhancing phishing email detection, which contributes to stronger organisational resilience
NIGERIA’S POLITICAL ECONOMY IN THE POST-SUBSIDY ERA: AN ASSESSMENT OF STRUCTURAL CONSTRAINTS TO NATIONAL DEVELOPMENT
No full textSince the rise of the nation-state and global trade, natural resources have significantly shaped national economies and played a key role in determining economic growth and power. In Africa, however, the presence of resources and wealth has often failed to translate into sustainable development. Nigeria, despite its vast oil reserves, remains trapped in underdevelopment due to systemic inefficiencies and over-reliance on crude oil. This study examines Nigeria’s post-subsidy political economy, with a focus on the 2023 fuel subsidy removal under the Tinubu administration. Although intended to spur growth and attract investment, the policy has coincided with rising poverty, inflation, and economic stagnation. The research explicitly examines how Nigeria’s reliance on oil revenue, corruption, and import dependence affects economic diversification, stability, and development. It also examines the relationships between unemployment, inequality, insecurity, and their combined impact on Nigeria’s socio-economic development. Drawing on resource curse and conflict theory, and employing a mixed-methods approach, the study identifies political and administrative corruption, as well as low productivity, as key structural barriers to economic transformation. It argues that political corruption erodes institutional capacity, while unproductivity stems from poor resource usage and lack of diversification. The study recommends institutional reform, particularly in fiscal oversight, to bridge the gap between policy goals and developmental outcomes in resource-rich states like Nigeria. By offering a critical reappraisal of Nigeria’s resource governance, this study contributes new insights into the persistent gap between policy intentions and developmental outcomes in resource-rich states
The effect of interactive magnetic board media on reading interest and reading skills of elementary school students.
No full textThis study investigates the influence of interactive magnetic board media on elementary students’ reading interest and reading skills. The purpose of the research is to address the low reading motivation and underdeveloped reading abilities often found among lower-grade primary school students. A mixed-methods approach was employed, integrating both quantitative and qualitative techniques. The research was conducted on first-grade students at SD Negeri Tlogowungu 02, with additional trials involving inclusive second-grade students at SD Negeri Trangkil 06. Data collection instruments included classroom observation, guided interviews, and analysis of students’ work. Quantitative data focused on changes in students’ reading skills before and after the intervention, while qualitative data were gathered through observations of students’ engagement and interest in reading activities. The findings revealed a significant positive effect of the interactive magnetic board media in enhancing both reading interest and reading skills among regular and inclusive students. The media’s use of colorful images, numbers, and letters proved effective in capturing students’ attention and sustaining their engagement during reading tasks. These results suggest that the interactive magnetic board is a child-friendly and inclusive instructional tool that can support literacy development in early primary education. The study contributes to the field by presenting an innovative, visually stimulating medium that can be integrated into classroom practice to foster student motivation and improve foundational reading competencies. 
SOCIOECONOMIC AND ACADEMIC DETERMINANTS OF STUDENT FEE REPAYMENT: EVIDENCE FROM UUM’S DISTANCE LEARNING PROGRAMME
No full textThe increasing burden of student debt, particularly within higher education institutions, presents a significant challenge for both students and funding institutions. This study seeks to identify the factors that contribute to students\u27 inability to repay their academic fees at Universiti Utara Malaysia. With cross-sectional survey of part -time students, this study analyses the likelihood of fee arrears based on student demographic factors – gender, gender, cumulative grade point average (CGPA), employment status, and marital status. This result of study shows that students with tuition fee arrears are ranging from RM1001 to RM 2000, most likely to be male and married with several years of working experience. By means of binary logistic regression, this study sheds light on how these characteristics influence the repayment behaviour, offering insights for targeted financial interventions. These results contribute strategic recommendation for educational institutional to address student debt effectively and refining financial aid repayment policies
Masalah penyebutan konsonan dan vokal bahasa Melayu dalam kalangan pelajar Thai: Kajian kes di Universiti Thammasat Bangkok, Thailand: (Malay consonant and vowel pronunciation problems among Thai students: A case study at Thammasat University Bangkok, Thailand)
No full textKajian ini membincangkan masalah sebutan bunyi konsonan dan vokal bahasa Melayu dalam kalangan pelajat Thai. Perbezaan antara konsonan dan vokal bahasa Melayu dan Bahasa Thai mewujudkan kerumitan dalam sebutan pelajar Thai yang mempelajari bahasa Melayu. Bunyi konsonan dan vokal dalam bahasa Thai yang berbeza jauh dengan bahasa Melayu hal ini telah menimbulkan masalah dalam kalangan pelajar Thai dalam menghasilkan vokal lain seperti konsonan /c/ /g/ /h/ /j/ /p/ /r/ /t/ dan masalah sebutan bunyi vokal seperti bunyi vokal /a/ disebut /o/ (bunyi vokal dalam bahasa Melayu dialek Patani) dan bunyi vokal /e/ didapati mereka keliru /e/ e-taling dengan /ə/ e-pepet. Kajian ini bertujuan untuk mengenal pasti masalah penyebutan konsonan dan vokal bahasa Melayu dalam kalangan pelajar Thai yang belajar mata pelajaran Bahasa Melayu (Elementary Bahasa Melayu 1). Kajian ini melibatkan seramai 20 orang pelajar sebagai responden. Jumlah tersebut terdiri daripada 14 orang pelajar bukan Muslim mereka tidak bertutur bahasa dialek Melayu Patani dan tidak pernah belajar bahasa Melayu, 3 orang pelajar Muslim yang tidak boleh bertutur dialek Melayu Patani dan juga 3 orang pelajar Muslim yang bertutur bahasa dialek Melayu Patani. Kajian ini juga meneliti faktor yang menjadi punca kepada masalah tersebut berlaku. Hasil kajian memperlihatkan beberapa masalah penyebutan bahasa Melayu bagi pelajar Thai yang mempelajari bahasa Melayu di Universiti Thammasat iaitu (I) penyebutan mengikut bunyi dialek Melayu Patani dalam kalangan pelajar yang boleh bertutur dialek Melayu Patani (II) kekeliruan penyebutan bunyi vokal /e/ e-taling dengan /ə/ e-pepet oleh pelajar yang bukan Muslim dan pelajar Muslim yang tidak boleh bertutur Dialek Melayu Patani (III) kekeliruan penyebutan konsonan dan vokal yang sama dengan bahasa Inggeris dalam kalangan pelajar yang bukan Muslim (IV) penyebutan secara salah bunyi konsonan yang tidak ada dalam bahasa Thai dan bahasa Inggeris dalam kalangan pelajar yang bukan Muslim dan (V) tidak berkemampuan menyebut konsonan penutup yang tidak terdapat dalam bahasa Thai dan bahasa Inggeris dalam kalangan pelajar yang bukan Muslim. Punca kesalahan penyebutan bunyi konsonal dan vokal bahasa Melayu dalam kalangan pelajar Thai ialah pengaruh dialek Melayu Patani dan pengaruh daripada bahasa asing lain iaitu bahasa Thai, bahasa Inggeris dan bahasa Arab
Peningkatan kemahiran terjemahan dan interpretasi dalam pembelajaran bahasa Indonesia dengan menggunakan teknik SDTI: (Improving translation and interpretation skills in learning Indonesian language using SDTI technique)
No full textKemahiran penterjemahan dan tafsiran sangat diperlukan oleh pelajar bahasa asing. Tahap kemahiran ini dapat dinilai melalui terjemahan dan tafsiran yang dihasilkan. Penyampaian yang tepat dan jelas oleh penterjemah dalam bentuk teks atau ucapan lisan sangat penting supaya pembaca atau pendengar dapat menerima mesej dengan berkesan. Teknik SDTI ialah pendekatan baharu yang dapat membantu pelajar meningkatkan kemahiran penterjemahan dan tafsiran. Artikel ini memperkenalkan teknik SDTI, iaitu Semak (Listening), Duplikasi (Shadowing), Terjemahan (Translation) dan Interpretasi (Interpretation) yang diaplikasikan kepada pelajar di Jabatan Tafsiran dan Terjemahan Bahasa Melayu-Indonesia di Hankuk University of Foreign Studies (HUFS). Responden kajian terdiri daripda enam pelajar HUFS yang mengikuti pengajian pada Semester Pertama 2024. Kajian memperlihatkan bahawa teknik SDTI dapat mendidik pelajar untuk menjadi penterjemah yang berkebolehan bagi menghasilkan terjemahan yang baik, tepat, mahir bertutur dan yakin dalam penyampaian mesej