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The rapid advancement of artificial intelligence (AI) has significantly altered the landscape of business decision-making, particularly for Micro, Small, and Medium Enterprises (MSMEs) in Indonesia. Despite their critical role in economic growth and employment generation, MSMEs frequently encounter substantial barriers related to technological adoption, digital literacy, and resource allocation. This research systematically investigates the utilization of artificial intelligence as a responsive innovation mechanism designed to optimize decision-making processes within the MSME sector. By employing a comprehensive systematic literature review methodology, analyzing peer-reviewed publications from globally recognized academic databases between 2018 and 2024, this study delineates the multifaceted benefits, complex challenges, and contextualized adoption strategies associated with AI integration. The findings reveal that artificial intelligence holds transformative potential for enhancing operational efficiency, refining predictive analytics, and sustaining long-term competitiveness. Conversely, critical challenges, including inadequate digital infrastructure, financial constraints, and organizational resistance, remain pervasive obstacles. Consequently, the study advocates for a gradual, responsive innovation approach that aligns technological adoption with specific business needs and environmental pressures. The insights derived from this research offer substantial theoretical and practical implications, providing a strategic framework for policymakers and MSME owners to accelerate digital transformation inclusively.

Digital transformation has positioned artificial intelligence (AI) as a major driver of organizational change and innovation. This study aims to analyze the influence of AI implementation on transformational leadership dynamics and the shifting role of leaders in managing human resources through a Systematic Literature Review of reputable studies published within the last five years. The findings indicate that AI acts as a catalyst in strengthening the dimensions of intellectual stimulation and individualized consideration through predictive analytics and talent personalization. The automation of administrative and repetitive tasks enables leaders to focus more on strategic vision, organizational innovation, decision-making, and emotional engagement with employees. However, the effectiveness of AI implementation is highly dependent on leaders’ digital literacy, adaptive capabilities, and readiness to integrate technology into organizational processes. This study contributes by proposing a hybrid leadership framework that combines artificial intelligence with human emotional intelligence to support more effective leadership practices. The practical implications emphasize the importance of leadership development that prioritizes empathy, ethical awareness, and algorithmic transparency in order to maintain trust, encourage sustainable innovation, and strengthen organizational resilience in increasingly dynamic and volatile environments.

This study examines the integration of technology in the process of Human Resource (HR) transformation through the perspective of employee data analytics as a strategic approach to modern HR management. The primary focus of the study is to analyze the impact of the simultaneous integration of digital HR systems and organizational digital transformation on improving the efficiency of HR functions, with organizational agility positioned as a moderating variable that strengthens this relationship. In addition, the study explores the potential optimization of Artificial Intelligence (AI) technologies and predictive analytics methods, such as Bayesian Optimization, in predicting workforce dynamics, including employee attrition risk and competency development needs, while also bridging the analytical skills gap among HR practitioners. The research method employed is a systematic literature review of relevant scientific publications from 2021 to 2025, selecting sources that address digital HR transformation, HR analytics, and the application of AI in organizational contexts. The findings indicate that digital HR systems have a strong and significant effect on enhancing operational efficiency and the quality of HR decision-making, and this effect becomes more optimal when supported by a high level of organizational agility. Furthermore, AI and predictive analytics are proven to generate more accurate predictions and simplify technical complexity, making them easier for HR practitioners to adopt. This study concludes that the success of HR transformation requires a holistic approach that aligns the use of advanced technologies with organizational capabilities, human resource readiness, and ethical considerations to create sustainable organizational value.

This research aims to critically examine the ethical integration of artificial intelligence (AI) in education through the perspective of maqāṣid al-sharīʿah, emphasizing the alignment between technological innovation and Islamic moral principles. The methods used are a systematic literature review and thematic content analysis against peer-reviewed publications for the period 2015–2025, which discuss the application of AI in primary, secondary, and higher education. The study identified dominant ethical issues, such as data privacy, algorithmic bias, accountability, human agency, and moral development, which were then mapped to Islamic ethical goals, including ʿadl (justice), amānah (belief), karāmah al-insān (human dignity), and ḥifẓ al-ʿaql (protection of reason). The results of the analysis show that the adoption of AI in education often emphasizes efficiency, personalization, and predictive analytics, but has the potential to reduce learners' autonomy and ethical reasoning. The mapping of maqāṣid al-sharīʿah shows a strong normative conformity, so that Islamic principles can be a moral foundation as well as a practical guide for AI governance. The research contribution is theoretical by bridging the literature on AI ethics and Islamic educational philosophy, as well as practical by offering an integrative framework for AI policymakers, educators, and developers. The integration of maqāṣid al-sharīʿah in AI governance ensures justice, trust, inclusivity, and the development of the whole human being (insān kāmil).

The increasing sophistication of cyber threats has rendered traditional cybersecurity models insufficient in safeguarding enterprise networks. This study introduces a risk aware cybersecurity governance model that integrates real time threat intelligence with predictive anomaly detection to proactively mitigate potential threats. By leveraging advanced machine learning and AI techniques, the model enhances the ability to identify and address cyber threats before they can escalate into significant incidents. The model’s ability to predict anomalies, analyze real time threat intelligence feeds, and provide early warnings allows for faster response times and reduced risk exposure compared to traditional reactive models. Through simulations and real-world use cases, the proposed model demonstrated a 30% reduction in response time and a 25% decrease in overall risk exposure, showing its potential to improve security decision-making and resilience in dynamic threat environments. Unlike traditional models that rely on static rules and periodic policies, the proposed model uses predictive analytics to stay ahead of evolving threats, ensuring continuous monitoring and rapid adaptation. This proactive approach enhances organizational resilience, particularly in handling sophisticated cyber threats such as ransomware, malware, and phishing attacks. Despite its effectiveness, challenges such as data overload, scalability, and the need for interpretability in AI models remain. Future research will focus on refining predictive models, improving scalability for larger networks, and enhancing the explainability of machine learning models to foster greater trust in automated cybersecurity systems. This study contributes to the ongoing evolution of cybersecurity governance by demonstrating the value of integrating predictive and real time monitoring technologies for enhanced threat detection and mitigation.

This study explores the transformative role of big data-driven Decision Support Systems (DSS) in global digital enterprises, particularly focusing on their impact on operational efficiency and corporate governance. By leveraging big data analytics, DSS offer organizations the tools to process vast amounts of real-time data, enabling executives to make more informed decisions that optimize resources, improve productivity, and reduce operational costs. The research highlights the integration of predictive analytics, machine learning, and real-time data processing within DSS, which allows businesses to gain strategic insights and anticipate market trends. Furthermore, the study emphasizes the significant role of DSS in enhancing corporate governance, improving transparency, accountability, and compliance with regulations. These systems foster better decision-making processes, which contribute to building trust among stakeholders and ensuring long-term organizational success. However, the study also identifies several challenges in implementing big data-driven DSS, including data management complexities, technological integration difficulties, and the need for skilled personnel. Despite these challenges, the findings demonstrate that big data-driven DSS are pivotal in driving competitive advantage, operational optimization, and governance improvements. The research concludes with actionable recommendations for executives to adopt and implement big data-driven DSS, emphasizing the importance of continuous support, training, and system integration. The study also suggests future research directions, including exploring the integration of emerging technologies like AI and IoT into DSS and assessing their long-term impact on sustainability and corporate governance.

This research explores the integration of Enterprise Architecture (EA) and Artificial Intelligence (AI) to optimize strategic decision-making in digital service-oriented organizations. These organizations often face challenges such as fragmented decision-making due to disconnected IT systems and limited data-driven insights. The objective of the study is to develop an integrated framework that combines EA and AI to enhance decision-making accuracy, operational efficiency, and strategic alignment. The study employs design science research methodology, involving the development of the framework, expert validation, and testing in simulated organizational scenarios. The findings reveal that the integrated framework improves decision-making by providing real-time, data-driven insights, predictive analytics, and better alignment with organizational goals. AI's role in analyzing large datasets and generating actionable insights allows decision-makers to anticipate future trends and make more informed decisions. The framework significantly outperforms traditional EA approaches, particularly in terms of predictive decision support and adaptive intelligence. The study concludes that the integration of EA and AI provides a robust solution for organizations looking to improve strategic decision-making, enhance operational efficiency, and stay competitive in dynamic business environments.

This research investigates the development of IT-based Automatic Identification System (AIS) data surveillance models supporting maritime safety through integration of advanced information technology, maritime engineering principles, and human factors optimization. AIS technology generates vast real-time vessel movement data creating unprecedented opportunities for safety enhancement through systematic surveillance, collision risk detection, traffic pattern analysis, and incident prevention, yet effectiveness depends critically on intelligent data processing algorithms, reliable IT infrastructure, and competent personnel capable of interpreting surveillance outputs and taking appropriate actions. Through qualitative analysis involving maritime safety authorities, vessel traffic service (VTS) operators, port authorities, marine engineers, IT specialists, data scientists, and maritime training institutions, this study examines how IT-based surveillance models incorporating pattern recognition, anomaly detection, predictive analytics, and crew-centered interfaces can transform maritime safety management from reactive incident response toward proactive risk prevention. Results demonstrate that intelligent AIS surveillance can identify 75-90% of high-risk situations 15-45 minutes before critical events, reduce collision risks by 60-80%, improve traffic management efficiency by 35-55%, and enhance crew situational awareness by 45-65% when integrated with appropriate training programs developing personnel competencies in data interpretation, system operation, and coordinated response. Key implementation challenges include data quality and completeness issues, computational infrastructure requirements, algorithm development complexity, personnel competency gaps requiring substantial training investments, organizational coordination barriers, and privacy/security concerns. Findings reveal that successful AIS surveillance implementation requires holistic sociotechnical approaches integrating IT systems engineering, maritime domain expertise, and human capability development through coordinated design, deployment, and training strategies. This research contributes to maritime safety literature by providing integrated frameworks for IT-based surveillance systems incorporating technical capabilities, operational requirements, and human factors supporting evidence-based safety management.

This research investigates the development of integrated operational systems connecting terminal and ship operations for docking and berthing time optimization through systematic analysis of historical data. Port efficiency depends critically on minimizing vessel turnaround time, with berth allocation, docking procedures, and cargo operations coordination determining overall port productivity and competitiveness. Through qualitative analysis involving port operators, terminal managers, ship agents, harbor masters, and operations research specialists, this study examines how historical operational data can inform intelligent coordination systems improving berthing efficiency. Results demonstrate that data-driven integration systems incorporating predictive analytics, automated scheduling, and coordinated workflows can reduce average berth turnaround time by 15-30%, improve berth utilization by 20-35%, and decrease operational conflicts by 40-60% through optimized allocation and proactive coordination. Key implementation challenges include data quality and availability, system integration complexity, organizational coordination barriers, and resistance to automated decision support. Findings reveal that historical data-based optimization represents transformative advancement from experience-based scheduling to evidence-driven operational planning supporting port efficiency enhancement, capacity maximization, and service reliability improvement. This research contributes to port operations literature by providing practical frameworks for data-driven berthing optimization applicable to diverse port operational contexts.

This study analyzes and designs a dual-role web-based ordering information system to optimize order management at Sunrise Bakery. This SME currently faces inefficiencies due to manual recording. The system, developed using the SDLC Waterfall method with PHP and MySQL, serves two main actors: customers, who can order online, browse catalogs, track orders, and pay digitally; and administrators (admin, cashier, owner), who manage products, update stock, input in-store orders, generate daily/monthly sales reports, and manage user access. Black Box Testing confirms all core functions work correctly. The system successfully addresses manual process shortcomings by improving data accuracy and providing real-time monitoring for both customers and management. It offers a comprehensive digital solution to enhance operational efficiency and service quality. Limitations include the lack of integrated digital payment gateways and external messaging. Future development should incorporate payment gateways (e.g., OVO, GoPay), WhatsApp notifications, a mobile application, and predictive analytics for sales and stock forecasting.

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.