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Diseases in primary health services exhibit complex spatial-temporal dynamics due to urbanization and population mobility. Conventional surveillance approaches are difficult to capture these patterns adaptively. Machine learning (ML) based on spatio-temporal modeling offers a solution with the ability to detect disease clusters automatically and with high precision. Research Objectives: This research aims to develop a machine learning model to detect disease hotspots from primary service data in Indonesia, with a focus on improving prediction accuracy, interpretability, and relevance of health policies. Methodology: The primary service dataset for 2024 (5,343 entries) was analyzed using three ML models Gradient Boosting Machine (GBM), Temporal Random Forest (TRF), and Multi-EigenSpot with spatial (village) and temporal (week, month) features. Performance evaluation includes predictive (AUC, F1-score) and spatial (Moran's I, Spatio-Temporal Correlation Index) metrics. Results: The results showed that Multi-EigenSpot achieved the best performance (AUC=0.91; F1=0.86), with the detection of dominant hotspots in Sungai Asam and Beringin Villages. Moran's I value of 0.63 indicates a strong spatial autocorrelation, while STCI=0.57 indicates moderate temporal stability. Conclusions: ML-based spatio-temporal models are effective in identifying hidden disease patterns and have the potential to be integrated into national digital surveillance systems. This approach supports precision public health by providing a scientific basis for real-time location- and time-based intervention policies.

This research investigates integrated maritime workforce resilience and mental health management frameworks addressing post-pandemic seafarer wellbeing challenges and organizational safety culture transformation. Through qualitative analysis involving 39 stakeholders including seafarers, ship operators, mental health professionals, maritime unions, training institutions, and maritime authorities, this study examines how COVID-19 pandemic intensified mental health crises through extended contracts, shore leave restrictions, and isolation while exposing systemic inadequacies in psychological support systems. Results demonstrate that comprehensive mental health frameworks can reduce psychological distress by 55-70%, improve safety performance by 40-55%, enhance crew retention by 45-60%, and decrease incident rates by 35-50% when integrating organizational culture change, leadership competency development, predictive analytics, and culturally-adapted interventions. Key challenges include mental health stigma (affecting 65-80% of seafarers), limited organizational investment (only 18-25% adequate), service accessibility gaps, and workforce demographic diversity requiring culturally-sensitive approaches. Findings reveal that effective mental health management requires systemic organizational transformation integrating psychological wellbeing into safety management systems, work design optimization, family support programs, and career sustainability rather than treating mental health as peripheral welfare concern, supporting maritime industry's workforce retention and operational safety imperatives.

Artificial Intelligence (AI) has increasingly shaped the digital transformation of higher education, particularly through its integration with Learning Management Systems (LMS). Features such as intelligent tutoring, predictive analytics, plagiarism detection, and automated grading are reshaping teaching and learning. However, questions remain regarding the readiness of higher education institutions and the acceptance among lecturers and students. This paper presents a Systematic Literature Review (SLR) of studies published between 2020 and 2025, focusing on readiness and acceptance of AI in LMS. Guided by the PRISMA framework, 220 records were identified, 85 screened, 40 assessed for eligibility, and 20 included in the final analysis. Findings highlight that readiness is largely influenced by infrastructure, digital literacy, and institutional policy, while acceptance is shaped by perceived usefulness, ease of use, trust, and behavioural intention. Although challenges such as ethics, cost, and privacy concerns persist, opportunities exist in the form of personalized learning and intelligent decision-making. The review concludes that while AI adoption in LMS is progressing globally, developing contexts such as Malaysian polytechnics require further research and targeted interventions to enhance both readiness and acceptance.

This study discusses the application of the K-Means Clustering algorithm in the car rental ordering system. The objective is to help group booking data based on certain patterns such as car type, booking frequency, and rental duration. The clustering results are expected to improve service efficiency and help companies better understand customer preferences. The research was conducted using historical car rental booking data from a rental company. The results show that the K-Means method can successfully cluster booking data into several useful clusters for business decision-making. This extended paper also explores theoretical concepts of clustering, related studies, limitations of the method, and potential future enhancements such as integrating predictive analytics. It highlights the importance of transforming large volumes of raw booking data into actionable business intelligence to support marketing strategies, fleet management, and customer segmentation.  

This paper explores the transformative influence of artificial intelligence (AI) on the accounting profession, particularly within the Accountant General of the Federation (OAGF). The research investigates how AI-driven innovations are reshaping traditional accounting practices and redefining the role of accountants. By conducting a systematic literature review, this study identifies three primary dimensions of AI’s impact: the automation of repetitive tasks such as data entry, transaction processing, and reconciliation; enhanced data analytics capabilities, which include predictive modeling and real-time decision support; and the evolution of accountants' roles toward more strategic and value-added activities, such as financial advisory and risk management. The automation of routine processes through AI allows accountants to focus on higher-level tasks that require judgment, creativity, and expertise, ultimately enhancing the overall efficiency of the accounting function. Furthermore, AI’s advanced data analytics tools provide more accurate insights, enabling accountants to offer more effective financial guidance and make more informed decisions. As AI reduces the time spent on manual processes, accounting professionals can improve their role in advising on business strategy, improving risk management, and identifying new growth opportunities. The study’s findings underscore the importance of embracing AI in the accounting profession, not only to improve operational efficiency, reduce costs, and scale operations but also to enable accountants to stay competitive in a rapidly evolving technological landscape. The paper concludes by emphasizing that adopting AI is essential for accountants to remain relevant and continue providing valuable contributions to their organizations. Future research should focus on the long-term implications of AI on accounting ethics and the development of necessary skills for accounting professionals to thrive in the age of AI.

The manufacturing industry faces major challenges in maintaining consistent product quality amidst the dynamics of technology and global competition. This study aims to develop an effective Business Intelligence (BI) implementation model to support data-based quality control. The method used is a conceptual design approach through integrated system simulation, including MySQL database, PHP backend, Power BI visualization, Google Cloud AutoML predictive analytics, and initial processing using Microsoft Excel. Historical production data for 12 months is used for model training and defect trend visualization. The simulation results show that the implementation of BI can reduce product defect rates, accelerate system response, and increase inspection process efficiency. Technical validation proves the model's prediction accuracy is above 90%, while field validation shows positive acceptance from users regarding the ease of use of the dashboard. This system not only supports early detection of quality deviations but also contributes to real-time strategic decision making. With an integrated technology approach, BI enables medium-sized manufacturing companies to adopt an adaptive and sustainable digital quality system, in line with the concept of Quality 4.0.

Introduction: The integration of Digital Twin (DT) technology and the Internet of Things (IoT) into Building Energy Management Systems (BEMS) offers a transformative approach to optimizing energy consumption in buildings. This study explores the development of a Digital Twin based BEMS prototype, which leverages real time data collection, predictive analytics, and machine learning to enhance energy efficiency, reduce costs, and support sustainability goals in modern buildings. The research also addresses key gaps in current energy management systems, including real time adaptive control and integration with smart grid platforms. Literature Review: Previous research highlights the limitations of traditional BEMS, which often rely on static control strategies and lack real time adaptability. Recent advancements, including predictive maintenance and machine learning integration, have improved energy optimization. However, challenges such as data interoperability, scalability, and cybersecurity remain. This review consolidates current approaches and identifies opportunities for enhancing BEMS through the integration of DT technology, IoT, and machine learning. Materials and Method: The methodology employed involves the design of a Digital Twin based BEMS prototype, incorporating IoT sensors for real time data collection on variables such as HVAC load, occupancy, and environmental factors. The system uses time series forecasting and adaptive control strategies to optimize energy consumption. A case study building is used for validation, with performance metrics such as energy savings, CO₂ footprint reduction, and peak load reduction assessed to evaluate the system's effectiveness. Results and Discussion: The results demonstrate a significant reduction in energy consumption (up to 50%) compared to traditional BEMS, along with improved forecasting accuracy and sustainability performance. The prototype achieved a high R² score in predicting energy usage, validated through real world application in the case study building. The economic feasibility analysis showed substantial cost savings and a strong return on investment, making the system a financially viable solution for energy efficient building management.

Aquaculture is a vital sector in global food production, providing essential protein sources. However, the industry faces significant challenges, including high energy consumption and environmental impact. The integration of renewable energy, particularly solar power, with automation and IoT systems offers a promising solution to enhance energy efficiency, sustainability, and productivity in aquaculture operations. This study aims to evaluate the effectiveness of solar powered autonomous systems in reducing energy usage, improving operational efficiency, and promoting environmental sustainability in aquaculture. Literature Review: Recent research has explored various technologies, such as Digital Twins (DTs) and Precision Fish Farming (PFF), which integrate IoT sensors for real time monitoring and optimization of fish farming operations. The combination of Artificial Intelligence (AI) and the Internet of Things (IoT), known as AIoT, has further advanced the industry by enabling automated decision making and predictive analytics. Solar power integration with IoT systems has been shown to significantly reduce operational costs, minimize carbon emissions, and enhance the sustainability of aquaculture practices. These advancements have the potential to address the challenges of energy consumption and environmental degradation in the industry. Materials and Method: This research utilizes a hybrid solar powered IoT system for aquaculture, integrating solar panels, IoT sensors, and automated control systems. The system monitors key water quality parameters, such as pH, dissolved oxygen, turbidity, and temperature, to maintain optimal conditions for aquatic life. Data is collected through IoT sensors and analyzed through a cloud-based platform. A pilot study is conducted on a small scale aquaculture farm to evaluate the system's performance, including energy consumption, water quality management, and fish health. Energy savings, operational efficiency, and environmental impact are assessed. Results and Discussion: The integration of solar powered IoT systems significantly reduced energy consumption compared to traditional systems, with a notable decrease in grid electricity reliance. The system successfully maintained optimal water quality conditions, enhancing fish health and growth. Solar powered systems proved reliable, even in regions with variable sunlight, and demonstrated improvements in operational efficiency through automation. The environmental benefits were evident, with a reduction in carbon emissions and lower operational costs. The study highlights the feasibility of solar powered IoT systems as a sustainable solution for modern aquaculture operations.

Given the increasing prevalence of mental health challenges in digital work settings, especially among IT remote workers, early detection mechanisms have become critically important. This study aims to improve the prediction accuracy of mental health conditions among IT remote workers by integrating feature engineering techniques within machine learning models. Five algorithms consisting of Random Forest, Logistic Regression, K-Nearest Neighbors, Decision Tree, and Naive Bayes were evaluated. The Random Forest model achieved the best performance, with 83% accuracy, 83% precision, 100% recall, and a 90% F1-score, followed closely by Logistic Regression with 82% accuracy. Nevertheless, the results demonstrate the feasibility of applying machine learning to support the early detection of mental health risks, offering a strong foundation for future research in predictive analytics and the development of intelligent support systems within digital work environments.

This research aims to develop an interactive web dashboard to support data analysis for vending machine sales. The dashboard is designed to facilitate the management of large datasets through intuitive visualizations and interactive features such as filtering, searching, and pagination. The development process involves several stages, including data collection, data cleaning, analysis, visualization, web design, implementation, and deployment using GitHub Pages. Technologies like HTML, CSS, JavaScript, Chart.js, and Grid.js are utilized to ensure efficiency and accessibility. The results of the research show that the dashboard effectively presents key information, such as sales trends, best-selling products, and payment method preferences, thereby supporting more accurate and data-driven strategic decision-making. However, the research has limitations in integrating predictive analytics. Future development is recommended to include predictive algorithms and test system performance on large-scale data. This solution is expected to contribute significantly to optimizing vending machine management and serve as a development model for similar applications in other business sectors.

This abstract explores the role of artificial intelligence (AI) in enhancing consumer satisfaction in Sichuan's online customer experience and service efficiency. With the rapid growth of e-commerce, understanding consumer preferences and behaviors has become crucial. AI technologies like chatbots, predictive analytics, and personalized recommendations are integrated into online platforms to streamline service delivery and improve user interactions. By leveraging data-driven insights, businesses can tailor their offerings to meet the specific needs of consumers, thereby increasing satisfaction levels. Furthermore, AI facilitates faster response times and more efficient problem resolution, leading to a seamless shopping experience. This study was conducted through an online questionnaire distributed to 380 Sichuan participants to measure their optimization and service efficiency satisfaction. The findings underscore that technical infrastructure, user acceptance and engagement, and service quality positively correlate with consumer satisfaction in AI experience improvement.

This research is a Systematic Literature Review (SLR) aimed at analyzing the application of Artificial Intelligence (AI) technology in the management of information technology (IT) projects. This study focuses on identifying the AI technologies employed, the benefits gained, and the challenges faced in implementing these technologies. The study gathers and analyzes literature from various leading databases, including Scopus, IEEE Xplore, and SpringerLink, within the timeframe of 2015–2025. The findings reveal that AI technologies such as machine learning, predictive analytics, and natural language processing play a significant role in improving efficiency, reducing risks, and supporting decision-making in IT project management. However, challenges such as data quality, organizational resistance, and implementation costs remain major obstacles in adopting this technology. This review provides comprehensive insights into trends, benefits, and barriers associated with AI utilization, along with recommendations for more effective implementation in the future.

As 6G networks promise unprecedented speeds and ultra-low latency, AI-based resource allocation plays a crucial role in optimizing network performance. This study explores AI-driven techniques for spectrum management, energy efficiency, and real-time data processing. By leveraging machine learning and deep learning models, AI enhances network adaptability, reduces congestion, and improves overall efficiency. The proposed approaches enable intelligent decision-making, dynamic resource allocation, and predictive analytics to meet the growing demands of future wireless communication. The findings highlight the potential of AI in revolutionizing 6G networks, ensuring seamless connectivity, and maximizing network capacity while minimizing power consumption. These advancements contribute to the development of more sustainable and intelligent telecommunication infrastructures.