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SR12 Herbal Cosmetics is a company engaged in the field of herbal and skin care. Founded in 2015 byToni Firmansyah, S. Farm., Apt. and Asrianty Salam, Farm. This company has a vision to provide benefits to many people through the herbal and skin care products they produce. SR12 Herbal Cosmetics products are formulated based on research from certified scientists, and have been tested at the Sucofindo Laboratory, are free of mercury and hydroquinone, and have been registered with the Indonesian Food and Drug Supervisory Agency (BPOM RI). SR12 Herbal Cosmetics has several factories in West Java Province and has an extensive distribution network with hundreds of distributors and tens of thousands of partners throughout Indonesia. The goal to be achieved is to produce a management information system model including a management information system for PT SR12 Herbal Cosmetics. The research object chosen is a company in the field of cosmetics and skin care which has its head office in Gunung Sindur, West Java. This selection aims to form a management information system design model that is able to produce relevant and timely information for planning, controlling, decision making and evaluating the performance of activities. For the Web-Based Instagram Content Management Information System Design project to Support SR12 Herbal Cosmetics' Brand Awareness, I used Agile (Scrum) due to the dynamic nature of digital marketing and potential changes to the Instagram API or business needs. This allowed SR12 to get core functionality faster and provide iterative feedback, ensuring the system built was truly relevant to their brand awareness needs.

Dijkstra's algorithm is one of the algorithms in graph theory that is used to solve the problem of the shortest path of a graph at each vertex that has a non-negative value. This algorithm was discovered by Edsger Wybe Dijkstra, a scientist from the Netherlands. The search for the shortest route for product delivery can be calculated through the application of the Dijkstra algorithm in the problem being faced. The problem of decision making for selecting the shortest route is still manual, so it experiences several obstacles, including the absence of a systematic and computerized system to assist the decision-making process in determining the route for shipping goods, the determination of shipping routes still depends on manual estimates so that the time taken between deliveries becomes inconsistent, the operational costs of shipping are relatively high because there is no optimal route determination system. Facing these problems, a system is needed that can minimize delays and increase effectiveness in shipping goods, namely determining the shortest route using the Dijkstra algorithm. This system works by finding various alternative routes for shipping goods at PT AMSA to address various structured and unstructured problems using data and models. To process this data and models, a method called the Dijkstra algorithm is required. Based on the description above, researchers will create a method for determining the shortest route for shipping goods at PT AMSA using the Dijkstra algorithm to facilitate the company's process of determining the shortest route.

The selection process for accepting new employees is one of the important stages in a company to ensure that the candidates accepted have qualifications that suit the company's needs. At WINMART, the selection process is still carried out manually, so it is less efficient and prone to errors. Therefore, a system is needed that can assist in more objective and efficient decision making. This Decision Support System (DSS) is designed to assist the selection process for recruiting new employees using the Simple Additive Weighting (SAW) method, which can assess several relevant criteria, such as work experience, education, skills and competency tests. This system was built on a web basis, so it can be easily accessed by parties involved in the selection process, such as HRD and managers. The SAW method was chosen because of its ability to convert various subjective criteria into more objective numerical scores, so that selection results can be more transparent and accountable. By using this system, it is hoped that it can increase efficiency, accuracy and transparency in the new employee selection process at WINMART, as well as facilitate decision making in selecting candidates who best suit the desired criteria.

The advancement of digital technology has significantly transformed organizational decision-making, particularly in modern leadership contexts that demand rapid and data-driven responses. Artificial Intelligence(AI) has emerged as a strategic technology capable of enhancing accuracy, speed, and effectiveness in decision-making through comprehensive data analysis. This study aims to analyze the role of AI in supporting leadership decision-making and its implications for organizational effectiveness using a narrative literature review approach. Secondary data comprising peer-reviewed national and international journal articles were analyzed to identify patterns, themes, and interactions between AI, leadership, and decision-making processes. The findings indicate that AI functions not only as a data analysis tool but also as a strategic element that strengthens leaders’ capabilities in evidence-based decision-making, improves team coordination, and optimizes organizational processes. Thematic synthesis identified three main domains analytics and predictive capabilities, leadership strategies, and implementation challenges that form the basis for integrating AI into managerial practice. This study contributes theoretically by expanding the digital leadership and technology-based decision-making framework and practically by providing guidance for organizations to optimize AI utilization to enhance decision quality and efficiency. The research also offers directions for future empirical studies to explore AI-leadership interactions across various organizational sectors, supporting more adaptive, effective, and data-driven decision-making in the digital era.

The development of Artificial Intelligence (AI) has transformed how business organizations manage operations, process information, and make strategic decisions, thereby creating a need for leadership patterns that are more adaptive, visionary, and responsive to digital transformation. This study aims to explain the concept of AI Leadership in the business context, identify why modern leadership must adapt to the advancement of AI, and analyze the leadership strategies that are relevant for navigating the era of AI-driven business. The study employs a Systematic Literature Review (SLR) approach with a narrative-thematic synthesis of relevant open-access scholarly literature on AI Leadership, digital leadership, human–AI collaboration, business transformation, and AI ethics. The findings show that AI Leadership represents a modern form of leadership that emphasizes not only technological understanding but also the ability of leaders to direct organizational change, foster data-driven decision making, manage collaboration between humans and technology, and implement responsible AI governance. The study also finds that the main strategies required by leaders in the AI era include strengthening digital literacy, developing adaptive managerial capabilities, enhancing human–AI collaboration, and integrating ethical principles into technology implementation. This article contributes conceptually by reinforcing AI Leadership as a leadership framework that is highly relevant to modern organizations and offers practical implications for the development of business leadership in the era of digital transformation.

This study aims to know the decision-making strategies in individuals with pragmatic personality. The approach used was qualitative with a case study method. The subjects of the study consisted of three individuals who were selected using purposive sampling techniques based on pragmatic personality characteristics, i.e. oriented to results, efficiency, and practical considerations in decision making. Data were collected through semi-structured interviews and observations, then analyzed using thematic analysis. The results of the study showed that all three respondents had relatively similar decision-making patterns, namely starting with understanding the problem, followed by identification and evaluation of multiple alternatives, as well as considering the advantages and disadvantages of each option. In determining the final decision, pragmatic individuals tend to choose solutions that are realistic, simple, effective, and have the lowest risk yet provide the optimal benefit. Additionally, in the face of important decisions, respondents showed a cautious attitude by calming themselves, not rushing, as well as partially considering the opinions of others as additional evaluation material. Overall, the findings of this study suggest that pragmatic personality plays a role in forming logical, adaptive, and real outcome-oriented decision-making strategies in daily life.

This study aims to analyze the role of assessment in decision-making within guidance and counseling services. Assessment plays a crucial role in providing accurate, objective, and comprehensive data regarding students’ characteristics, needs, and problems. This research employs a qualitative approach using a literature review method by analyzing 25 relevant scholarly sources related to assessment, measurement, and counseling services. The findings indicate that measurement serves as the fundamental basis of assessment, which involves systematic processes to obtain quantitative data. The quality of assessment results is highly influenced by the reliability and validity of the instruments used. Reliable instruments ensure consistency, while valid instruments ensure accuracy in measuring intended variables. Furthermore, assessment functions as a basis for diagnosis, planning, implementation, and evaluation of guidance and counseling services. The study also reveals that the application of assessment in schools is not yet optimal due to several factors, including limited counselor competence and lack of effective use of instruments. In addition, the integration of technology and the use of both test and non-test instruments have been shown to improve the accuracy and effectiveness of assessment. In conclusion, systematic and well-implemented assessment significantly enhances the quality of decision-making in guidance and counseling services. Therefore, improving counselors’ competencies and optimizing the use of assessment instruments are essential to support more effective counseling practices

This quantitative study examines the influence of information quality, work engagement, and leadership style on decision making effectiveness among employees of a manufacturing company. Data were collected from 100 respondents using accidental sampling technique. Multiple linear regression analysis revealed that all three independent variables simultaneously affect decision making effectiveness. Partially, information quality, work engagement, and leadership style each have significant positive effects on decision making effectiveness. Leadership style demonstrates the strongest influence, followed by information quality and work engagement. The regression equation indicates positive contributions from all predictors. The coefficient of determination shows that the three predictors collectively explain a substantial portion of variance in decision making effectiveness. Findings suggest manufacturing companies should prioritize transformational leadership development programs, conduct information system audits, and implement work engagement initiatives to enhance decision making effectiveness. Future research should expand to other industries, add organizational culture variables, and employ longitudinal designs.

This study explores the integration of community driven learning practices in the adoption of educational technology and its impact on sustainability. With the rapid advancement of digital tools and platforms, higher education institutions have increasingly adopted online and hybrid learning models to enhance teaching and learning. However, despite the potential benefits, aligning institutional goals with community needs remains a significant challenge. This research utilizes a mixed methods approach, combining stakeholder surveys, policy analysis, and comparative case studies to evaluate the effectiveness of both top down and community aligned adoption models. The findings reveal that community driven models, which involve local stakeholders in the decision making process, lead to higher engagement, better adoption rates, and greater long term sustainability compared to top down approaches. Stakeholders, including educators, students, and administrators, reported that participatory decision making fostered a sense of ownership and ensured the relevance of adopted technologies. The study also identifies key sustainability factors, including participatory decision making, long term community engagement, and contextual relevance, which are crucial for ensuring that educational technologies remain effective and beneficial over time. However, challenges such as resistance to change, lack of resources, and unequal access to technology were found to hinder the successful implementation of sustainable models. The research concludes with practical recommendations for educational institutions and policymakers to adopt community aligned models and ensure equitable access to technology. Future research directions are suggested to further explore the balance between institutional innovation and community driven learning, with a focus on long term outcomes and the adaptability of these models across different educational contexts.

This study aims to analyze consumer purchasing behavior on e-commerce platforms using the Decision Tree algorithm as an easily interpretable classification method. The dataset used consists of 12,330 transaction records with 18 attributes representing visitor characteristics and user activities during interactions with the e-commerce platform. The research stages include data exploration to identify initial patterns, data preprocessing to handle missing values and class imbalance, splitting the data into training and testing sets, training the Decision Tree model, evaluating model performance, and visualizing the tree structure to analyze decision rules.The test results show that the Decision Tree model with a maximum depth of 3 achieves fairly good performance, with an average accuracy of 89.78%, precision of 69.82%, recall of 59.95%, and an F1-score of 64.51% for the buyer class. The visualization of the decision tree provides clear interpretation of the main attributes influencing purchasing decisions, thereby facilitating understanding for non-technical decision makers. Overall, this study demonstrates that the Decision Tree method is effective in modeling consumer purchasing behavior in e-commerce and can be utilized as a basis for data-driven business decision making, particularly in marketing strategies and improving sales conversion rates.

The development of Artificial Intelligence (AI) technology has significantly changed strategy and decision making in marketing management. However, the massive use of AI raises new challenges regarding ethics, transparency and governance. This research aims to analyze the impact of using AI, especially recommender systems and large language models (LLMs), on the effectiveness of marketing decisions, as well as the role of AI governance in controlling emerging ethical issues. The research method uses a quantitative approach with Structural Equation Modeling (SEM) analysis of data collected from 250 marketing professionals in Indonesia. The research results show that the use of AI has a significant positive effect on the effectiveness of marketing decisions (β=0.62, p<0.001), but also raises ethical issues (β=0.48, p<0.01). Ethical issues were proven to reduce the effectiveness of marketing decisions (β=-0.31, p<0.05), while good AI governance was able to moderate the negative impact of ethical issues (β=0.27, p<0.05). These findings underscore the importance of AI governance in building effective and ethical marketing systems.

Decision-making under uncertainty is a major challenge in management, economics, and public policy, where outcomes cannot be accurately predicted due to limited information and environmental dynamics. This article conducts a systematic literature review of risk and probability approaches to decision-making under uncertainty, focusing on rational theory synthesis (such as expected utility theory, decision tree analysis, and Bayesian decision theory) and behavioral perspectives (prospect theory and heuristics). The review covers reputable literature from the last ten years to the present. The results show that the probabilistic approach provides a strong and adaptive rational framework, but has significant limitations due to cognitive biases such as loss aversion, overconfidence, and ambiguity aversion, which cause deviations from normative rationality. The integration of rational approaches with behavioral elements, through hybrid models, has proven to be more comprehensive and realistic for dealing with true uncertainty (Knightian uncertainty). These findings emphasize the need for a multidimensional decision-making paradigm in organizational and policy practices.

This study aims to analyze the factors influencing people's decisions to use a Home Ownership Credit (KPR) with a murabahah contract at a sharia bank in Tebing Tinggi City. The research method used is a descriptive qualitative approach, with data collection techniques through observation, in depth interviews, and documentation of people who have used the murabahah KPR product. Informants were selected purposively to obtain relevant and in depth data. The results show that people's decisions are influenced by several main factors, including compliance with sharia principles, price transparency, certainty of fixed installments, ease of financing processes, promotions conducted by the bank, housing location, and the brand image of the sharia bank. The murabahah contract is considered to provide clarity of profit margins from the start and stability of installment payments throughout the financing period, thereby increasing public security and trust. These findings indicate that consistent application of sharia principles and appropriate marketing strategies play a significant role in increasing public interest in sharia housing financing products.

Sustainable tourism village development requires effective multi-party collaboration between government, private sector, and communities. This study aims to analyze the implementation of Collaborative Governance in the Bakti BCA Program as a reference for accelerating village tourism in Tuban Regency. The research method used is a qualitative approach with data collection techniques through in-depth interviews, observation, and document analysis at four assisted locations namely Bektiharjo Tourism Village, Kelapa Beach, Geger Tourism Village, and Sowan Beach. The results showed that the Bakti BCA Program has successfully implemented Collaborative Governance principles through inclusive multi-stakeholder involvement, formal and structured collaboration processes, consensus-based decision making, and shared value creation. The program had a significant positive impact with a 428% increase in total tourist visits, 559% growth in MSMEs, and 156% increase in community income during the 2021-2024 period. The collaboration between PT Bank Central Asia Tbk, Tuban Regency Government, and local communities can serve as a reference model for sustainable tourism village development in other regions with strengthening self-reliance strategies for long-term sustainability.

Stock price crash risk has become a critical concern in investment decision making and risk management, drawing the attention of investors and regulators amid a dynamic global business environment and repeated financial crises. However, empirical evidence on this issue remains limited in developing countries, particularly in the Iraqi context. Therefore, this study examines the relationship between board characteristics—board gender diversity, board size, and board independence—and stock price crash risk, as well as the mediating role of audit committee effectiveness. The study uses secondary data from ten banks listed on the Iraq Stock Exchange (ISX) during the 2017–2023 period. The findings show that board gender diversity and board size significantly reduce stock price crash risk. Higher female representation on boards is associated with more conservative decision making and stronger monitoring, which improves financial reporting transparency. An appropriately sized board also enhances oversight and lowers the likelihood of extreme negative stock price movements. In addition, the results indicate that the frequency of audit committee meetings mediates the relationship between board independence and stock price crash risk, suggesting that board independence is more effective when supported by an active audit committee. This study recommends that investors and financial analysts consider board characteristics and audit committee effectiveness when assessing firm value and risk. Furthermore, regulators and policymakers are encouraged to promote gender diversity on corporate boards to strengthen governance quality and reduce the probability of stock price crashes.

Makan Bergizi Gratis (MBG) program is a national policy designed to improve the nutritional quality of children and support human resource development. However, its implementation at the regional level still faces various institutional and coordination issues. This study aims to analyze the implementation of the Free Nutritious Meals program in Jambi City, focusing on the dynamics of program implementation and the inhibiting factors. This study uses a qualitative approach with a case study method. Data were collected through in-depth interviews with policy implementers and  documentation with informants determined through purposive sampling. Data analysis was conducted using Merilee S. Grindlee policy implementation theory through data reduction, presentation, and conclusion drawing. The results of the study indicate that the implementation of the MBG program in Jambi City has not been running optimally. This condition is characterized by the strong dominance of the central government in the decision making process, weak coordination between actors at the regional level, and inadequate readiness of supporting institutions, especially in aspects of human resources, monitoring system, and clarity of operational standards for implementation.

The rapid growth of e-commerce in Indonesia has encouraged businesses to adopt digital marketing strategies supported by effective information management. Marketplace platforms such as Shopee provide extensive marketing data with strategic value; however, its utilization requires a structured system. This study aims to analyze the implementation of Marketing Information Systems in supporting digital marketing strategies on the Shopee e-commerce platform. The research employs a qualitative descriptive approach through literature review and indirect observation using internet-based scholarly sources. Data were collected from relevant national and international journals and analyzed descriptively to identify the roles, components, and influencing factors of Marketing Information Systems. The results indicate that Marketing Information Systems play a significant role in supporting digital marketing decision-making through the management of internal records, marketing intelligence, marketing research, and decision support systems. Furthermore, the implementation of Marketing Information Systems on Shopee is influenced by information technology development, company size and scale, consumer behavior, competitive environment, and corporate strategy and objectives. This study is expected to contribute theoretically to the development of digital marketing studies and provide practical references for e-commerce businesses in optimizing data-driven marketing strategies.

Real time decision making applications, such as those used in autonomous vehicles, smart cities, and industrial IoT, require fast, scalable, and accurate analytics to ensure timely responses and optimized operations. Traditional cloud-based systems face significant challenges in meeting these requirements due to high latency, limited scalability, and bottlenecks in data processing. This study explores the use of a hybrid Edge Cloud architecture to optimize End to end machine learning (ML) pipelines for real time applications. The proposed system offloads time-sensitive tasks to edge devices, while computationally intensive processes are handled by the cloud, ensuring efficient use of resources and reduced latency. Experimental results demonstrate that the hybrid model reduces inference latency by up to 70% compared to cloud-only systems, while maintaining model accuracy and increasing throughput. Additionally, the scalability of the hybrid architecture is highlighted, as it can handle large-scale data streams and adapt to varying workloads. The findings show that hybrid Edge Cloud architectures are well-suited for applications where fast decision making is critical, such as autonomous systems and real time analytics in smart cities. However, challenges remain in managing resources across edge and cloud systems, particularly in balancing computational loads and ensuring system reliability. Future research should focus on optimizing task partitioning, integrating advanced edge AI models, and exploring the use of 5G networks to enhance performance further. Overall, the study demonstrates the potential of hybrid Edge Cloud systems in overcoming the limitations of traditional cloud-based ML pipelines and provides insights into the future of real time data processing.

Autonomous decision-making systems increasingly rely on complex artificial intelligence models to operate in dynamic and safety-critical environments. While these models provide strong predictive capabilities, their black-box nature limits transparency, trust, and accountability. This study proposes a structured research methodology for integrating Explainable Artificial Intelligence (XAI) into autonomous decision-making systems. The research adopts a conceptual–analytical approach to develop an explainability-oriented framework that embeds transparency across perception, decision-making, and action execution stages. The methodology includes literature-driven problem identification, conceptual framework construction, classification and mapping of XAI methods, and formulation of explainability evaluation criteria. The results demonstrate that effective explainability in autonomous systems requires a hybrid integration strategy, combining in-model transparency with post-hoc explanation mechanisms. A structured mapping of XAI techniques to autonomous system components and a conceptual decision-flow diagram are presented to illustrate explainability integration. The findings highlight that layered and context-aware explainability enhances system interpretability, supports human oversight, and improves safety relevance without compromising autonomous operation. This study contributes a reusable methodological foundation for the design and evaluation of explainable autonomous systems, offering practical guidance for future empirical validation and real-world deployment in safety-critical applications.

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.