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Algorithmic technologies are widely used in contemporary marketing strategies due to the growth of the digital economy. Digital companies can evaluate consumer activity data in real time and provide highly personalized digital experiences thanks to artificial intelligence-based solutions, especially machine learning. In addition to examining how algorithmic governance and surveillance capitalism affect algorithmic personalization, this study looks into how these mechanisms affect consumer engagement, purchase intention, and perceptions of hyperreality within the digital market ecosystem. 356 active users of digital platforms, such as social media and e-commerce, were surveyed as part of this study's quantitative methodology. The links between the constructs in the suggested conceptual model were examined through data analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that the development of algorithmic personalization systems is strongly influenced by data-driven capitalism practices and algorithmic governance. Additionally, it has been demonstrated that algorithmic personalization improves customers' sense of hyperreality and increases their interaction with digital platforms. Additionally, the study shows that the most powerful factor influencing purchase intention is consumer interaction. By combining viewpoints from technology, the political economics of data, and hyperreality theory into a thorough empirical framework, these findings add to the body of knowledge on digital marketing.

This study examines customer churn prediction in subscription-based telecommunications from a digital marketing perspective using machine learning. The analysis utilizes a secondary dataset of 7,043 customer records that simulate behavioral, contractual, and financial attributes commonly found in telecom services. Three classification algorithms Logistic Regression, Random Forest, and Gradient Boosting are applied to model churn behavior. Data preprocessing includes handling missing values, encoding categorical variables, and splitting data into training and testing sets. Model performance is evaluated using accuracy, recall, and ROC-AUC, with emphasis on recall due to its importance in identifying at-risk customers. The results show that Gradient Boosting achieves the highest overall performance with an ROC-AUC of 0.84, while Logistic Regression provides relatively higher recall. Key drivers of churn include short-term contracts, higher monthly charges, and lower service engagement. However, recall remains moderate, indicating limitations in capturing complex behavioral factors. These findings suggest the need to combine predictive models with behavioral insights and highlight the importance of early customer engagement and long-term retention strategies.

Customer churn is a strategic challenge for digital streaming platforms because it directly Impacts revenue and business sustainability. This study aims to analyze the factors influencing customer Churn and develop a churn prediction model using the Random Forest algorithm. The study uses a Quantitative approach with an explanatory design and utilizes secondary data from the Netflix Customer Churn and Engagement Dataset available on Kaggle. The dataset consists of 1,000 customer data with 16 Variables covering demographic characteristics, service usage behavior, financial condition, and customer Satisfaction level. The data was processed through preprocessing, one-hot encoding, and a 70:30 split Between training and test data. Model performance was evaluated using accuracy, precision, recall, F1 Score, and ROC-AUC metrics. The results show that the Random Forest model produces an accuracy of 53.7%, precision of 56.3%, recall of 63.6%, F1-score of 59.7%, and ROC-AUC of 0.534, indicating Moderate predictive ability and only slightly better than random classification. Feature importanceAn.evealed that user engagement levels, such as viewing duration and frequency of interactions, Were the most dominant factors influencing churn, followed by economic factors and customer satisfaction. The results of this study are expected to provide a basis for streaming platforms to design more effective Customer retention strategies.

Because it directly impacts revenue, customer loyalty, and long-term business sustainability, customer churn is a critical issue for the e-commerce industry. High churn rates indicate that a business is unable to retain existing customers, which means it is more expensive to acquire new customers. Therefore, a precise analytical approach is needed to identify customer behavior patterns that are likely to churn. Using machine learning methods, this study analyzes and predicts customer churn. For this study, the E-Commerce Customer Churn 2025 dataset, obtained from Kaggle, was used. This dataset consists of 10,000 customer data and contains fifteen variables covering transaction behavior, customer characteristics, and churn status. Data preprocessing, descriptive analysis, exploratory data analysis (EDA), and classification model development using Logistic Regression and Random Forest algorithms were part of the research project. Model evaluation was conducted using a Confusion Matrix and Receiver Operating Characteristic (ROC) Curve to evaluate the model's accuracy and ability to distinguish between churned and non-churned customers. The results showed that the Random Forest model performed better than Logistic Regression, with an ROC-AUC of 1.00. Furthermore, feature importance analysis revealed that the days_since_last_purchase variable was the most dominant factor in predicting customer churn. These findings are expected to help e-commerce companies design more effective, data-driven customer retention strategies.  

Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.

Economic forecasting is becoming increasingly important year after year, especially during crises such as the pandemic of COVID-19 and the Russia-Ukraine war. Its development can be seen from the use of basic statistical models to the increasingly widespread use of machine learning technology. Economic forecasting plays an important role in helping to formulate policies and is also a reliable tool for researchers in dealing with uncertainty. Global crises, such as inflationary pressures due to the pandemic and supply chain disruptions from the Russia-Ukraine conflict, have prompted increased research in this field in an effort to anticipate economic shocks and emphasize the urgency of forecasting to prepare strategies for dealing with future uncertainty. This literature review uses the Scopus database with 2561 publications from 2020 to 2025, analyzed using R Studio with a bibliometrix approach (specifically biblioshiny) and VOSviewer to map relevant thematic connections. This analysis shows that economic forecasting is greatly influenced by market uncertainty and geopolitical factors, and at the same time influences public policy formulation and financial stability. Research contributions from Indonesia are still limited, with only 40 documents, thus emphasizing the need to strengthen economic forecasting studies in Indonesia to support monetary policy and national financial stability.

The interval between 2018 and 2025 represents a defining epoch in financial assurance, characterized by a systemic collision between traditional audit methodologies and the exponential sophistication of fraudulent actors. This research employs a comprehensive library research methodology, utilizing Systematic Literature Review (SLR) to evaluate the evolving landscape of audit and fraud. The study traces the theoretical migration from Cressey’s Fraud Triangle to multidimensional frameworks like the Fraud Pentagon, which emphasizes the roles of arrogance and competence. Through a forensic examination of catastrophic audit failures including Wirecard, FTX, and the emerging risks of crypto-assets, the research identifies recurring patterns of auditor failure in assessing operational risks and internal controls. Furthermore, the report analyzes the dual-edged impact of Artificial Intelligence (AI); while machine learning algorithms offer enhanced detection capabilities, the rise of Generative AI (GenAI) and deepfake technology has empowered perpetrators to execute sophisticated "synthetic reality" frauds. The study critically evaluates regulatory responses, particularly the revision of International Standard on Auditing (ISA) 240, which mandates a more proactive "fraud lens." The findings suggest that the auditing profession faces an existential crisis of relevance, necessitating a fundamental shift toward a forensic mindset supported by advanced technological integration.

This paper explores the epistemological dimensions of the digital transformation occurring in traditional game development through the integration of machine learning systems. By examining how knowledge creation, validation, and application have evolved in this domain, we identify fundamental shifts in the epistemological frameworks governing game development practices. The research investigates how machine learning has redefined creative processes, technical implementation, and experiential design while challenging traditional notions of authorship, expertise, and knowledge transmission. Through analysis of industry case studies, technological capabilities, and theoretical frameworks, this paper contributes to understanding how machine learning systems are not merely tools but epistemological agents that fundamentally transform how knowledge is generated, validated, and utilized in game development ecosystems.

Research on digital payment systems has grown rapidly over the past decades; however, comprehensive and in‑depth studies that synthesize existing empirical findings remain limited. This study aims to conduct a systematic literature review and bibliometric analysis on digital payment research based on empirical publications indexed in Scopus. Using the keyword “Digital Payment” in the article title, abstract, and keywords, a total of 485 documents published between 1989 and 2025 were identified. The evaluation was conducted on November 30, 2025, and the collected data were analyzed using bibliometric techniques with VOSviewer software. The findings reveal a significant surge in digital payment research beginning in 2016, with its peak occurring during 2023–2025, in line with the accelerating digital economic transformation worldwide. Knowledge production has shifted toward emerging economies—particularly India, Indonesia, and Malaysia—supported by dense inter‑institutional and inter‑author collaboration networks. Research themes have expanded beyond technical payment system aspects to interdisciplinary issues involving technology, finance, financial inclusion, human behavior, public policy, and the application of machine learning for security and system optimization. Journal sources, affiliations, authors, and keyword analyses confirm that digital payments have become essential infrastructure for the modern economy and a rich empirical domain for advanced studies on financial stability, consumer protection, regulation, and digital financial innovation.

The purpose of this study is to investigate the strategic role of air transportation management in Military Operations Other Than War (MOOTW), particularly in archipelagic contexts such as Indonesia, where rapid humanitarian response, territorial surveillance, and civil–military cooperation are essential for resilience. By applying a Systematic Literature Review (SLR), this article synthesizes findings on humanitarian logistics, technological transformation, and policy frameworks for strengthening national defense readiness. Design/methodology/approach – This study employs a qualitative Systematic Literature Review (SLR) methodology guided by PRISMA principles, analyzing 30 scholarly contributions from 2009–2025, including international peer-reviewed journals, Routledge and Springer volumes, arXiv preprints, and Indonesian academic publications.Results highlight that strategic air  transportation is indispensable for disaster relief, medical evacuation, and supply delivery in archipelagic nations. The adoption of AI, machine learning, UAVs, and reinforcement learning has enhanced responsiveness and equity in humanitarian supply chains. However, persistent challenges include aging fleets, interoperability constraints, and fragmented civil–military coordination. The study underscores the need for modernization of air assets, institutionalized civil–military collaboration, and integration of AI-based routing and command systems. Strengthening these aspects can enhance Indonesia’s resilience and preparedness in MOOTW scenarios. This article uniquely bridges global research on data-driven air power with Indonesian defense perspectives, proposing a scalable strategic framework for air transportation management that advances archipelagic resilience.

Economic forecasting is becoming increasingly important year after year, especially during crises such as the pandemic of COVID-19 and the Russia-Ukraine war. Its development can be seen from the use of basic statistical models to the increasingly widespread use of machine learning technology. Economic forecasting plays an important role in helping to formulate policies and is also a reliable tool for researchers in dealing with uncertainty. Global crises, such as inflationary pressures due to the pandemic and supply chain disruptions from the Russia-Ukraine conflict, have prompted increased research in this field in an effort to anticipate economic shocks and emphasize the urgency of forecasting to prepare strategies for dealing with future uncertainty. This literature review uses the Scopus database with 2561 publications from 2020 to 2025, analyzed using R Studio with a bibliometrix approach (specifically biblioshiny) and VOSviewer to map relevant thematic connections. This analysis shows that economic forecasting is greatly influenced by market uncertainty and geopolitical factors, and at the same time influences public policy formulation and financial stability. Research contributions from Indonesia are still limited, with only 40 documents, thus emphasizing the need to strengthen economic forecasting studies in Indonesia to support monetary policy and national financial stability.

The rapid growth of social media has yielded vast digital traces with high potential for improving corporate forensic auditing. Their utilization, however, lags behind through technological reliability, privacy, and adherence to the law. The aim of this study is to explore effective utilization of social media digital traces in forensic auditing and develop a functional framework that lags neither behind through technological efficiency nor adherence to the law and ethics. A mixed-method design was utilized, combining quantitative machine learning analysis with qualitative document analysis and semi-structured interview insight. Quantitative data drawn from social media digital traces were processed using Random Forest algorithm with SMOTE for class balancing, while qualitative data were processed using thematic analysis. The results indicated high model performance with 91.3% accuracy and AUC-ROC of 0.94, together with three emergent themes: digital integration, ethics and privacy, and regulation and legality. The results demonstrate that digital footprints may serve as an effective early and reliable indicator for fraud detection, provided they are accompanied by clear regulatory and ethical frameworks. Its principal contribution lies in the development of an operational model that combines machine learning with legal and ethical perspectives, a new strategy which matures methodological refinement and practical application in today's forensic auditing.

Information technology is a technology used to manage data, including processing, acquiring, organizing, storing, and manipulating data in various ways to produce high-quality information—namely, information that is relevant, accurate, and timely. This information is used for personal, business, and governmental purposes, serving as strategic information in decision-making. To anticipate changes in weather conditions, particularly rainfall, a valid and accurate report is needed that can be useful for the public. So far, the correlation or relationship between the factors influencing weather conditions—especially rainfall—has not been precisely determined, making it mathematically difficult to create a model that can describe the correlation among all these factors. This is where Artificial Neural Networks (ANN) come into play: to create such models and map out the existing problems purely based on the input data provided. One of the capabilities of neural networks is to make predictions based on previously learned data using the backpropagation method.

This study presents a machine learning approach for predicting the dimensions of microstrip antenna slots based on antenna performance parameters such as frequency, gain, directivity, return loss (S11), radiation efficiency, and VSWR. A two-phase methodology was employed. In the first phase, ten regression algorithms were evaluated, and Random Forest was identified as the most effective model based on Mean Absolute Error (MAE) and R-squared (R²) scores. In the second phase, hyperparameter tuning was conducted using Grid Search to further improve the model’s performance. The optimized Random Forest model demonstrated consistent improvements in predictive accuracy, with R² values increasing across all output variables. These results indicate that the combination of regression-based modeling and systematic hyperparameter tuning is effective for capturing complex relationships in antenna design tasks. The proposed approach offers a promising data-driven alternative for geometric prediction in microstrip antenna development, particularly when analytical models are insufficient.

The research aimed to analyze the relationship between the financial literacy of financial report users and the required level of disclosure, and to propose an intelligent mechanism based on artificial intelligence techniques to customize report content. The research variables were identified as: financial literacy (high, medium, low) and disclosure level (basic, medium, advanced), with a study of the effects of age and educational level. The research adopted a mixed methodology (descriptive and analytical) using machine learning models (Random Forest) and analysis of a questionnaire consisting of 15 questions. The research sample included 150 participants from Baghdad Bank and Asia Islamic Bank of Iraq, with 155 questionnaires distributed (electronically and on paper), excluding 5 incomplete questionnaires. The results showed a strong positive correlation between financial literacy and the level of disclosure, where 75% of those with high financial literacy preferred advanced reports. The artificial intelligence model also recorded an accuracy of 82% in predicting the optimal level of disclosure. The research recommended adopting intelligent customizable financial reports through artificial intelligence and enhancing users' financial literacy to improve decision quality.

Artificial Intelligence (AI) is quickly becoming a game changer in the way businesses build and manage their strategies. This article explores how AI is helping organizations make faster and smarter decisions, streamline operations, and spark innovation across various industries. With the ability to process massive amounts of data, AI tools can uncover valuable insights about market trends and customer behavior, allowing companies to respond more accurately and stay ahead of the competition. From machine learning and generative AI to natural language processing and digital twins, these technologies are transforming everything from internal workflows to how businesses connect with customers. The article also offers a practical roadmap for adopting AI in a business setting, covering steps like evaluating readiness, running pilot projects, and measuring success through return on investment (ROI). It emphasizes the need for strong data infrastructure, skilled teams, and a culture that supports innovation and data-driven thinking. Challenges such as algorithmic bias, data privacy, and internal resistance to change are also addressed. Real-world examples from banking, retail, and manufacturing show how AI can deliver real impact improving efficiency, increasing customer satisfaction, and driving business growth. Ultimately, embracing AI isn’t just about keeping up with technology it’s about shaping the future of smart, strategic, and ethical business.

This research conducted a systematic literature review of studies related to analyzing service quality based on user reviews with a machine learning approach. A total of 15 international and national journals were analyzed to identify challenges, methods, and trends in research in this aspect. The review results show that Natural Language Processing (NLP) and Sentiment Analysis techniques are the dominant approaches, with machine learning models such as Deep Learning, Naive Bayes, and Support Vector Machine (SVM) being commonly used. The review also identifies research gaps and provides recommendations for future research directions.

Artificial Intelligence (AI) has become an essential tool in the world of management for decision-making. This article examines the ways in which AI can be used to improve the quality and speed of decision-making, and how AI can improve the operational efficiency of companies. In addition, this article also examines the challenges and opportunities that companies face in adopting AI.In the rapidly evolving digital era, AI has become an essential component of modern business strategies. Today's managers are often faced with the challenge of analyzing very large and complex volumes of data. To make good and timely decisions, AI offers a potential solution with fast and precise data analysis capabilities.The use of AI in decision-making involves machine learning algorithms and models to efficiently process and analyze large amounts of data. This helps managers gain deeper and more accurate insights, enabling more effective decision-making.

International Micro, Small and Medium Enterprises (MSMEs) face significant challenges in improving global competitiveness due to limited resources and access to effective market analysis, despite contributing 45% to the global economy (OECD, 2025). This research aims to develop an integrated machine learning (ML) model with a mixed-methods approach to optimise cross-border MSME market analysis. A combination of quantitative (transaction data analysis of 500 Indonesian export MSMEs 2020-2024 using XGBoost and SEM-AMOS) and qualitative (interviews with 15 MSME players) methods revealed that the XGBoost model achieved 89% accuracy in predicting market trends, with key variables including social media sentiment (28%) and exchange rate fluctuations (19%). Qualitative results show that 65% of MSMEs face cross-border regulatory barriers that ML models do not detect. The findings extend the Resource-Based View theory by validating AI-driven market intelligence as a strategic asset (β = 0.67, p 0.7. This research highlights the importance of technology integration and contextual adaptation in the digital transformation of MSMEs.

Despite their 45% contribution to the global economy, international micro, small, and medium-sized enterprises (MSMEs) face considerable obstacles in enhancing their global competitiveness because they lack the resources and access to efficient market analysis (OECD, 2025). In order to optimize cross-border MSME market analysis, this research attempts to construct a machine learning (ML) model coupled with a mixed-methods approach. A combination of quantitative (XGBoost and SEM-AMOS were used to analyze transaction data of 500 Indonesian export MSMEs 2020–2024) and qualitative (interviews with 15 MSME players) methods showed that the XGBoost model achieved 89% accuracy in predicting market trends, with key variables including exchange rate fluctuations (19%) and social media sentiment (28%). According to qualitative findings, the ML model does not identify cross-border regulatory constraints that 65% of MSMEs must deal with. These results validate market intelligence powered by AI as a strategic asset, extending the Resource-Based View paradigm. The significance of contextual adaptation and technological integration in the digital transformation of MSMEs is emphasized by this study.