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The growth of information technology has driven changes in consumer behavior, one of which is through e-commerce platforms such as Shopee. This phenomenon has generated a large number of customer reviews, including those for local cosmetic products such as Wardah. These reviews serve as an important source of information for understanding customer perceptions and satisfaction levels. However, manual analysis of large and linguistically diverse datasets is inefficient and potentially subjective. This study aims to implement the multi-category Naive Bayes algorithm to classify the sentiment of Wardah product reviews on Shopee into three categories: positive, negative, and neutral. The data were collected using a web scraping technique and processed through a series of preprocessing stages including case folding, tokenization, stopword removal, stemming, and text cleaning. Subsequently, term weighting was performed using the TF-IDF method prior to classification. Model performance was evaluated using a confusion matrix as well as accuracy, precision, and recall metrics. The results indicate that the multi-category Naive Bayes algorithm achieved an accuracy of 86.00%, a precision of 86.63%, and a recall of 98.24%. This approach can assist business practitioners in objectively understanding customer opinions and support decision-making in business strategy and product development.

The rapid growth of digital technology and social media has reshaped communication practices, consumer behavior, and marketing approaches, particularly among Generation Z, who are widely recognized as digital natives. For this generation, social media serves as a major channel for obtaining information and exploring product references before making purchase decisions. In this environment, influencer marketing has emerged as a prominent promotional approach because it can foster audience interaction, emotional connection, and trust more effectively than conventional advertising. This study examines the role of influencer marketing in shaping the digital behavior of Generation Z in Indonesia. A qualitative literature review method was employed by analyzing secondary sources drawn from academic journals, books, and related publications. The findings indicate that influencer marketing has a meaningful influence on information-seeking behavior, decision-making processes, and the level of digital engagement among Generation Z. Influencers are commonly viewed as more credible, relatable, and authentic sources of information. Furthermore, the effectiveness of influencer marketing is determined more by credibility, authenticity, and the quality of interaction than by follower count alone. These findings suggest that companies should adopt digital marketing strategies that are more interactive, personalized, and relationship-oriented in order to engage Generation Z more effectively.

This study aims to evaluate how petty cash management practices contribute to improving operational efficiency at PT Anugerah Langgeng Berkat Abadi. This research focuses on examining the implementation of the petty cash management system, applied procedures, and its impact on the smooth execution of daily operational activities. The study employs a descriptive qualitative approach, with data collected through interviews, direct observation, and documentation during the internship period. The collected data were analyzed systematically to describe the actual condition of petty cash management within the company. The results indicate that PT Anugerah Langgeng Berkat Abadi implements a fluctuating fund system in managing petty cash. Expenditures are initially recorded manually and then re-entered into the company’s internal digital system to maintain control and accountability. Petty cash is used to finance routine and urgent operational needs, such as office stationery, transportation costs, and other short-term expenditures. The company has established standard operating procedures governing the use, recording, and accountability of petty cash. Several challenges were identified, including delays in the disbursement and reimbursement process, which may affect time efficiency. However, overall, the petty cash management system is considered effective in supporting short-term operational needs without disrupting the stability of the company’s main cash. This study concludes that systematic and well-controlled petty cash management plays an important role in the company’s cost efficiency strategy and supports daily operational activities. These findings align with strategic management principles, where appropriate financial decision-making contributes to the achievement of long-term organizational objectives.

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.

This study conducts a systematic review of literature concerning the role of Management Information Systems (MIS) in managing Umrah pilgrim data across Indonesia. Employing a Systematic Literature Review (SLR) guided by the PRISMA protocol, the study examined and synthesized 24 peer-reviewed articles published between 2019 and 2026, drawn from Google Scholar, SINTA, and nationally accredited journal portals. Findings indicate that MIS adoption has yielded notable improvements in operational efficiency, data reliability, service transparency, and the overall quality of managerial decision-making within Umrah travel organizations. Web-based MIS remains the predominant technological approach, offering integrated modules for pilgrim registration, document handling, financial tracking, and departure scheduling. At the national level, government-initiated platforms such as SISKOHAT and SISKOPATUH have demonstrated tangible contributions to the administration of Hajj and Umrah services. Nevertheless, persistent obstacles continue to hinder full-scale adoption, including inadequate digital infrastructure in certain regions, inconsistent internet connectivity, and gaps in human resource competencies for operating digital systems. The study argues that embracing MIS is no longer optional for Umrah travel operators; rather, it constitutes a strategic necessity. Future research is encouraged to investigate long-term implementation outcomes, cross-system comparisons, and data security considerations within the broader digital landscape of pilgrimage service management.

The development of digital technology and internet penetration are driving changes in consumer behavior and demanding that MSMEs not only adapt but also understand the operational motives and marketing channel integration strategies to face competition in the digital era. This study aims to examine the operational motives and technical adaptation processes of Micro, Small, and Medium Enterprises (MSMEs) in Bali in integrating online and offline marketing channels through an omnichannel strategy to respond to intense market competition in the digital era. This descriptive qualitative research uses a multiple case study approach in three different MSME sectors: goods maintenance services, creative services, and fashion retail. Data collection was conducted through direct observation, in-depth interviews, and a review of digital activities, then analyzed through single-case and cross-case stages. The results show that MSMEs optimize digital channels as instruments to expand reach and trigger consumer emotional engagement through a visual storytelling approach, while physical stores are crucially maintained as points of sensory validation. Functional integration has been proven to accommodate the shift in consumer behavior towards a hybrid decision-making pattern. In this pattern, consumers conduct information searches and initial evaluations online, but tend to shift to offline interactions to validate quality and finalize transactions to minimize purchase risk. In conclusion, cross-channel operational synergy has proven essential in boosting the competitive advantage of MSMEs.

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.

.This study aims to analyse the role of Financial Information Systems (FIS) in supporting risk management, decision-making, and organisational performance in the digital transformation era. This study employs the Systematic Literature Review (SLR) method to examine articles indexed in Scopus from 2016 to 2026. The PRISMA framework is used to ensure a systematic, transparent article selection process, resulting in the selection of 37 relevant articles for further analysis. The results of the study show that Financial Information Systems make a major contribution to improving financial transparency, operational efficiency, the quality of strategic decision-making, and organisational risk mitigation. In addition, the integration of emerging technologies such as Artificial Intelligence (AI), FinTech, big data analytics, and cloud computing further strengthens the effectiveness of financial information systems in modern organisations. This study contributes theoretically by mapping research trends and identifying research gaps, while providing practical benefits for organisations seeking to increase competitiveness through digital financial systems. For future research, it is recommended to develop a more predictive and intelligent Financial Information Systems model to address future business dynamics.

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 aims to examine the effect of working capital efficiency and leverage on profitability and its implications for firm value in manufacturing companies listed on the Indonesia Stock Exchange (IDX) during the 2020–2025 period. The post-COVID-19 pandemic condition has increased operational risks for manufacturing companies due to fluctuations in interest rates, exchange rates, cash management, inventories, and receivables. Therefore, companies are required to implement more effective financial strategies to maintain competitiveness. Profitability is positioned as an intervening variable because previous studies showed inconsistent results regarding the relationship between working capital efficiency, leverage, profitability, and firm value. This research uses a quantitative approach with path analysis to examine direct and indirect relationships among variables. The population consists of all manufacturing companies listed on the IDX, while the sample includes 45 companies selected from 270 firms using purposive sampling based on specific criteria, such as consistent listing and financial performance. The results indicate that working capital efficiency has a significant positive effect on profitability, leverage has a significant negative effect on profitability, profitability significantly increases firm value, and profitability fully mediates the effect of working capital efficiency and leverage on firm value. These findings provide theoretical and practical implications for managers and investors in financial decision-making.

Micro, small, and medium enterprises (MSMEs) are crucial for driving regional economic expansion. Many MSMEs have not yet optimally utilized accounting data in selecting investments. This study aims to analyze the influence of accounting knowledge, entrepreneurial traits, and subjective norms on the use of accounting information in investment decision-making among MSMEs in Semarang City. Using a quantitative approach, this study surveyed 80 MSMEs selected through purposive sampling. Data were obtained through questionnaires with a five-point Likert scale and analyzed using multiple linear regression with the help of SPSS after undergoing validity, reliability, and classical assumption tests. The results showed that partially accounting knowledge (t = 3.337; sig = 0.001) and entrepreneurial traits (t = 2.272; sig = 0.026) had a positive and significant effect on the use of accounting information, while subjective norms had no significant effect (t = -0.788; sig = 0.433). Simultaneously, the three independent variables significantly influence the use of accounting information (F = 5.306; p = 0.002) with a coefficient of determination (R²) of 0.173. This finding indicates that increasing accounting and entrepreneurial knowledge can encourage the use of accounting information in investment decision-making in MSMEs.

This study aims to analyze the effect of Good Corporate Governance (GCG), Firm Size, and Sales Growth on Financial Performance at PT Ace Hardware Indonesia Tbk listed on the Indonesia Stock Exchange (IDX) during the 2015–2024 period. Good Corporate Governance (GCG) in this study is proxied by institutional ownership, financial performance is measured using Return on Assets (ROA), firm size is measured by the natural logarithm of total assets, and sales growth is measured using the sales growth ratio. This study employed a quantitative method with a descriptive approach. The data used were secondary data in the form of annual financial statements obtained from the official websites of the IDX and the company. Data analysis techniques included descriptive statistics, classical assumption tests, multiple and simple linear regression analysis, and hypothesis testing consisting of t-test, F-test, and coefficient of determination with the assistance of SPSS version 27 software. The results of the study indicate that partially, the Good Corporate Governance (GCG) variable has a t-value of -1.526 < t-table 2.447, meaning that it has no significant effect on financial performance. The firm size variable has a t-value of -2.857 > t-table 2.447, indicating a significant negative effect on the company’s financial performance. The sales growth variable has a t-value of 1.593 < t-table 2.447, meaning that it has no significant effect on financial performance. Simultaneously, Good Corporate Governance (GCG), firm size, and sales growth have a significant effect on financial performance, with an F-value of 13.023 > F-table 4.76 and a significance value of 0.005 < 0.05. This study is expected to provide consideration for management and investors in decision-making and serve as a reference for future research in related fields.

This research is motivated by the increasing role of Artificial Intelligence (AI) in organizational transformation and the crucial function of leadership in ensuring its successful implementation. The primary objective of this study is to analyze the relationship between leadership, AI integration, and organizational performance, as well as to identify various challenges and supporting factors in the process. This study employs a systematic literature review (SLR) method by examining 30 relevant, reputable scientific articles from the Scopus and Google Scholar databases within the 2020–2026 timeframe through selection, evaluation, and thematic synthesis processes. The results indicate that AI integration significantly contributes to improving operational efficiency, data-driven decision-making quality, and organizational innovation. However, this success heavily depends on the role of adaptive, transformational, and digitally-oriented leadership capable of steering the technological vision. Conversely, major challenges were identified, such as employee resistance, limited digital competencies, and ethical issues surrounding data privacy. This study contributes to strengthening the conceptual understanding of leadership's role as a bridge between technology and organizational performance, while offering practical implications for management in designing effective, inclusive, and sustainable digital transformation strategies.

Bureaucratic rigidity is a form of public administration pathology that remains a serious problem in Indonesian governance. The bureaucracy's rigid, hierarchical nature, slow decision-making, and resistance to change are major obstacles to the government's digital transformation process. Amidst the demands of the digital era, which prioritizes speed, transparency, and efficiency, an unadaptive bureaucracy actually slows down innovation in public services and erodes public trust in the government. This study aims to analyze bureaucratic rigidity as a disease in public administration and its impact on the implementation of digital transformation in Indonesia. The method used is a qualitative approach with a literature review. The results indicate that bureaucratic rigidity is caused by a conservative organizational culture, complicated regulations, and low human resource capacity to adapt to technology. Furthermore, a lack of system integration and weak transformational leadership exacerbate these conditions. Digital transformation is not merely a technological issue, but a paradigm shift in governance. Therefore, comprehensive bureaucratic reform is needed, including simplifying procedures, strengthening the digital competency of civil servants, and shifting the work culture to be more flexible and innovative. Thus, an adaptive and responsive bureaucracy can be realized as the foundation of an effective digital government that is oriented towards public services.

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.

Leadership in educational management has a strategic role in determining the success of educational implementation. This study aims to examine the integrative paradigm between psychology and leadership in educational management, focusing on the psychological dimensions of educational leadership. This research employs a qualitative approach using a systematic literature review method. Data were collected from scientific articles published between 2020 and 2026 through Google Scholar using Publish or Perish. The findings indicate that the integration of psychological perspectives into leadership significantly enhances organizational effectiveness, particularly in fostering work motivation, strengthening interpersonal communication, and improving adaptive decision-making. Furthermore, psychological approaches contribute to conflict management and the development of a positive organizational climate. The study also highlights that leadership effectiveness is strongly influenced by psychological characteristics such as personality, emotional intelligence, and empathy. Therefore, integrating psychology into leadership serves as a strategic foundation for advancing educational management and improving human resource performance sustainably.

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.

The coffee shop industry in Indonesia is experiencing rapid growth that requires business owners to optimize data-driven strategies. This study aims to analyze customer preferences at Semanis Coffee and Resto using data mining methods  to support more effective business decision-making. The method used is Market Basket Analysis with the FP-Growth algorithm for association rule mining and the K-Means algorithm for customer segmentation. The research data consists of 672 sales transactions during the March-May 2025 period. The results of the association analysis with a minimum support of 0.004 and a minimum confidence of 0.2 resulted in five valid rules with a lift ratio above 1. The strongest rule is the combination of Americano→Milk Choco with a confidence of 42.9% and an elevator ratio of 5.229, indicating a strong linkage between products. The most popular products are Milk Choco (10.8%) and Americano (8.5%). Customer segmentation analysis identified three clusters: Cluster 0 (Loyal Customers) 80% with high frequency but low transaction value; Cluster 1 (Occasional Customers) 10% with low activity; and Cluster 2 (Large Buyers) 10% with high transaction value but low frequency. This study concludes that product bundling strategies, loyalty programs, reactivation campaigns, and premium services can be applied to increase the effectiveness of coffee shop businesses.

Inappropriate complementary feeding (MPASI) practices remain a public health concern, particularly during the first 1,000 days of life, as they contribute to the risk of growth disorders such as stunting. This study aimed to analyze the relationship between socioeconomic factors and maternal decision-making processes with complementary feeding practices based on consumer behavior theory. This study employed an observational analytic design with a cross-sectional approach involving 240 mothers with children aged 6–23 months in the working area of Puskesmas Putat Jaya, Surabaya. Data were collected through structured interviews and analyzed using the Chi-Square test. The results showed significant relationships between socioeconomic status and decision-making processes, as well as between decision-making processes and complementary feeding practices (p<0.05). Mothers with higher socioeconomic status tended to have better decision-making processes and more appropriate feeding practices. However, overall practices were not fully aligned with recommended standards. These findings indicate that improving complementary feeding practices requires not only knowledge enhancement but also strengthening maternal decision-making capacity within their socioeconomic context.

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