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Social media has evolved into a significant platform where consumers freely express their opinions, experiences, and levels of satisfaction regarding various products, including those offered by Micro, Small, and Medium Enterprises (MSMEs). The comments and reviews shared by customers on these platforms contain diverse sentiments that can serve as valuable indicators of how consumers perceive product quality. Understanding these sentiments is crucial for MSME owners, as it allows them to evaluate their products and adapt to market expectations more effectively. This study aims to analyze customer sentiment toward MSME products on social media by utilizing the Naïve Bayes algorithm, a widely used classification method in text mining. The data used in this research consist of customer comments collected from various social media platforms. The research process involves several stages, including data collection, manual labeling of sentiments, text preprocessing (such as tokenization, case folding, and stopword removal), and splitting the dataset into training and testing subsets. Subsequently, the classification process is carried out using the Naïve Bayes algorithm to categorize sentiments into positive, negative, and neutral classes. The results of this study demonstrate that the Naïve Bayes method is effective in classifying customer sentiments with a satisfactory level of accuracy. These findings provide a comprehensive overview of consumer perceptions regarding the quality of MSME products. Furthermore, this research is expected to assist MSME business owners in understanding customer feedback more systematically and using it as a basis for improving product quality and enhancing customer satisfaction in a competitive digital marketplace.

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.

Student dropout in higher education remains a persistent socioeconomic challenge, yet many predictive models reported in the literature are methodologically compromised by randomized cross-validation schemes that introduce temporal data leakage and artificially inflate predictive performance. This study proposes a longitudinal prescriptive learning analytics framework integrating three complementary methodological components: a Leave-One-Cohort-Out (LOCO) temporal validation protocol, a hybrid SMOTE-ENN class balancing strategy, and temporal velocity feature engineering derived from Learning Management System (LMS) behavioral trajectories. The framework was evaluated on a longitudinal dataset comprising 464,739 enrollment records and 77 features. Five predictive algorithms—XGBoost, LightGBM, CatBoost, Random Forest, and Logistic Regression—were comparatively assessed on a strictly isolated blind holdout cohort (2022), with CatBoost emerging as the champion estimator, achieving a PR-AUC of 0.8859, a Macro F1-Score of 0.9143, and the lowest Brier Score (0.0221), thereby demonstrating superior calibration and discriminative capability under severe class imbalance (93:7 ratio). Comprehensive ablation analysis revealed that temporal velocity features function not merely as additive predictors, but as a structural prerequisite enabling Synthetic Minority Oversampling Technique with Edited Nearest Neighbors (SMOTE-ENN) to generate high-quality synthetic boundary instances; removing these features reduced minority-class precision from 0.8302 to 0.6721. To operationalize predictive outputs into actionable intervention pathways, Diverse Counterfactual Explanations (DiCE) were implemented under a three-tier causal constraint architecture on 96 borderline high-risk students, generating 384 feasible intervention scenarios exclusively targeting forward-looking behavioral velocity metrics without constraint violations. Collectively, these findings advance the paradigm of prescriptive learning analytics by providing educational institutions with interpretable risk diagnostics and operationally feasible intervention guidance grounded in empirically validated behavioral and temporal dynamics.

Advances in digital technology have transformed the paradigm of faith proclamation, requiring the Church to optimize social media as a relevant catechetical instrument. This study aims to examine how the use of the TikTok platform can be optimized as an effective means of creative catechesis in connecting complex faith teachings for people in the digital era. The research method used is descriptive qualitative with a literature study technique, where data is collected from various scientific literature, Church documents, and relevant library sources. The research findings indicate that TikTok is an effective digital space for catechesis because it is supported by attractive audio-visual features, interactive features such as stitches and comments that enable two-way dialogue, and an algorithmic system that expands the reach of proclamation. The strategy of catechesis through short videos has proven to be able to change the perception of faith teaching that has become rigid to a more personal spiritual experience that is easily understood by all levels of society, especially the younger generation. The implications of this study emphasize the need for the Church to consistently adapt to digital culture and increase content creativity to ensure the continuity of inclusive evangelization amidst the dynamics of modern developments.

This article examines the relationship between Artificial Intelligence (AI), the concept of sensus communis proposed by Nicholas of Cusa (1401–1464), and the formation of religious communities. Through a theoretical-philosophical analysis, the author argues that sensus communis as the integrative capacity of the human intellect that unifies sensory, rational, and intuitive dimensions offers a normative epistemological framework for critically addressing the reductionism inherent in algorithmic AI. The main finding indicates that AI constitutes only a partial simulacrum of the integrative capacity of human reason and, therefore, cannot replace the ontological-transcendental dimension of authentic formation. Accordingly, this article proposes a model of critical-integrative formation grounded in three pillars: the selective use of AI, the preservation of AI-free spaces, and hermeneutical integration. The relevance of Cusa’s thought for contemporary religious formation is articulated in three contributions: docta ignorantia as a formative habitus, coincidentia oppositorum as a paradigm of dialogue, and ontological participation as the foundation of knowledge.

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.

The crisis of authority in global culture is a complex phenomenon reflecting a fundamental shift in how humans understand truth, values, and the sources of legitimacy of knowledge. This research focuses on the dynamics of epistemological change in global society influenced by digital technology, cultural relativism, and value pluralism. The main issue studied is how this crisis of authority influences the construction of truth and value systems in contemporary society, as well as its implications for social and spiritual life. The aim of this research is to develop an interdisciplinary analysis that integrates perspectives from philosophy, sociology, theology, and media studies in understanding this phenomenon. The method used is a qualitative approach based on systematic literature studies with a critical analysis of recent academic sources. The main findings indicate that the crisis of authority is characterized not only by the weakening of traditional institutions but also by the emergence of "alternative authorities" based on algorithms, public opinion, and subjective experience. This results in the fragmentation of truth, the relativization of values, and a crisis of epistemological legitimacy. The synthesis of this research confirms that the reconstruction of authority requires an integrative approach that combines rationality, ethics, and spiritual dimensions. In conclusion, the crisis of authority in global culture is not merely a challenge, but rather an opportunity to build a new epistemological paradigm that is more holistic, reflective, and rooted in transcendent values.  

 Cryptocurrency price prediction, particularly for highly volatile assets like Solana (SOL), is a crucial challenge in time series data analysis in digital finance. This study aims to compare the performance of the XGBoost machine learning algorithm with the Temporal Fusion Transformer (TFT) deep learning model in predicting Solana's daily closing price. The dataset used consists of historical Solana price data and network fundamentals data in the form of Total Value Locked (TVL). The research process includes data preprocessing, dividing training and test data, model training, and evaluation using the Root Mean Squared Error (RMSE) metric. The results show that using the same-day price feature has the potential to cause target leakage, resulting in invalid prediction accuracy. In testing using pure historical data without data leakage, the XGBoost model performed better than TFT with an RMSE of 4.27, while TFT produced an RMSE of 18.59. Furthermore, the integration of network fundamentals data in the form of TVL did not improve prediction accuracy and even caused a decrease in performance for the XGBoost model with an RMSE of 7.10. The results of this study show that the use of historical price action features is more effective than fundamental network indicators for short-term daily Solana price predictions.

The increasing complexity of urban road networks demands intelligent navigation systems capable of determining optimal routes efficiently. This research implements the Dijkstra Shortest Path algorithm to optimize route search on a location navigation system in Medan City. The system models a road network as a weighted graph comprising 57 strategic locations and over 90 road connections, represented using adjacency list data structures. The Dijkstra algorithm, implemented in Python using the heapq module for priority queue management, achieves an optimal time complexity of O((V+E) log V). The system features five main functions: shortest route search, popular routes, location listing, dynamic location addition, and dynamic road connection addition. System testing using a case study from Kualanamu Airport to the University of North Sumatra (USU) yielded an optimal route of 16.5 km through 4 road segments. Results demonstrate that the system successfully determines the most efficient route, provides accurate distance and travel time information for multiple transport modes (motorcycle, car, walking), and presents step-by-step journey guidance. This research contributes as a practical reference for applying shortest path algorithms in urban areas and serves as a foundation for developing more complex navigation applications in the future.

The development of artificial intelligence (AI) has transformed organizational landscapes and driven fundamental changes in leadership practices and strategic management. This article aims to critically examine AI-based leadership by highlighting its opportunities, risks, and implications for strategic management. The study employs a qualitative literature-based approach using an integrative literature review strategy. The data consist of secondary scholarly literature relevant to AI, leadership, governance, innovation, and strategic management, which were analyzed through qualitative thematic analysis and conceptual content analysis. The findings show that AI-based leadership creates opportunities in the form of improved decision quality, faster strategic analysis, operational efficiency, stronger innovation, and enhanced organizational agility. However, AI integration also presents risks, including algorithmic bias, limited decision transparency, technological dependency, accountability challenges, and ethical concerns. This study confirms that AI does not fully replace human leaders; rather, it fosters a hybrid leadership model that requires technical, adaptive, transformational, and ethical capabilities. The study implies that the effectiveness of AI-based leadership depends on responsible governance, organizational cultural readiness, and balanced human–machine collaboration in supporting strategic management.

The distribution of social assistance in Indonesia faces challenges such as inaccurate recipient data, overlapping programs, and limitations of traditional data management systems that lead to inaccurate targeting of aid. This study proposes a social assistance distribution optimization system using the Greedy algorithm that assesses recipient priorities based on economic conditions, number of family members, location, and urgency of needs with certain weights to produce objective rankings. This system is implemented in a JavaScript-based web application without external frameworks, making it lightweight and easily accessible. Simulations with 20 prospective recipients and a quota of 10 slots and validation with a dataset of 10,000 entries show that the Greedy algorithm produces identical results to Dynamic Programming but is much faster (669 times faster). In terms of complexity, this algorithm has O(n log n) time and O(n) space, and meets the requirements of the Greedy Choice Property and Optimal Substructure, making it a practical and efficient solution for managing large-scale social assistance distribution in Indonesia.

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.

The rapid digital transformation in educational institutions demands an efficient student grade data processing system capable of handling workloads responsively. This study aims to analyze and compare the efficiency of sorting algorithms (Merge Sort and Quick Sort) and searching algorithms (Linear Search and Binary Search) on a web-based platform. The research method employed is laboratory experimental, testing algorithm performance across various data volume stratifications, ranging from 50 to 1000 entities, using the V8 JavaScript engine. Research findings indicate that Quick Sort possesses superior speed compared to Merge Sort due to its efficient in-place sorting architecture, which minimizes memory overhead and Garbage Collection activity. Furthermore, a performance anomaly was discovered where the Just-In-Time (JIT) Compiler mechanism optimizes execution on large data volumes through a warm-up phase. In the searching aspect, Binary Search demonstrates superior O(log n) logarithmic stability compared to Linear Search, which risks causing interface freezing on massive data. The implication of this study is the critical importance of implementing data pre-sorting protocols to exploit logarithmic search speeds to ensure academic information system scalability. The integration of appropriate algorithms proves to be a crucial foundation in maintaining web application responsiveness amidst the ever-increasing escalation of educational data.

This study examines the role of digital philanthropy in fostering social solidarity in Indonesia through community-based social actions. The rapid growth of digital philanthropic practices in the post-pandemic era, mediated by social media and online platforms, has transformed collective humanitarian engagement. This study aims to explore how digital philanthropy contributes to the formation of social solidarity and social cohesion. A qualitative approach using a case study method was employed. Data were collected through in-depth interviews with fifteen community-based philanthropic actors, participant observation, and social media document analysis. Data analysis followed Miles and Huberman’s interactive model, including data reduction, data display, and conclusion drawing. The findings reveal that digital philanthropy strengthens social solidarity through digital empathy, trust-building, and collective participation. Social media functions as an inclusive interactive space that expands cross-group solidarity networks. However, challenges such as digital inequality and algorithmic bias remain significant. This study concludes that digital philanthropy holds strategic potential to enhance social solidarity when supported by inclusive and sustainable governance frameworks.

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.

This study implements the Damerau-Levenshtein algorithm for an Indonesian spelling checking and correction system based on the distance editing approach. The main objective of this study is to develop a system capable of automatically detecting and correcting spelling errors at the character level through a matching process against the KBBI dictionary and the Indonesian corpus. The methods used include data collection, text pre-processing, system design, and implementation of the Damerau-Levenshtein algorithm which includes insertion, deletion, substitution, and transposition operations. Testing was conducted using 25 test data consisting of standard words and modified words for typographical errors. The results show that the system is able to measure all test data with an accuracy level of 100% on a limited dataset. In addition, the average Damerau-Levenshtein Distance value of 0.84 indicates that most errors are in the light category. Evaluation using a confusion matrix produces precision, recall, and F1-score values ​​of 100% each. These results indicate that the Damerau-Levenshtein algorithm is effective in handling character-based spelling errors. However, the system still has limitations in handling complex semantic contexts and language variations. Therefore, further research is recommended to integrate language model-based approaches to improve the system's accuracy and generalization on real-world data.

This study implements the Damerau-Levenshtein algorithm for an Indonesian spelling checking and correction system based on the distance editing approach. The main objective of this study is to develop a system capable of automatically detecting and correcting spelling errors at the character level through a matching process against the KBBI dictionary and the Indonesian corpus. The methods used include data collection, text pre-processing, system design, and implementation of the Damerau-Levenshtein algorithm which includes insertion, deletion, substitution, and transposition operations. Testing was conducted using 25 test data consisting of standard words and modified words for typographical errors. The results show that the system is able to measure all test data with an accuracy level of 100% on a limited dataset. In addition, the average Damerau-Levenshtein Distance value of 0.84 indicates that most errors are in the light category. Evaluation using a confusion matrix produces precision, recall, and F1-score values ​​of 100% each. These results indicate that the Damerau-Levenshtein algorithm is effective in handling character-based spelling errors. However, the system still has limitations in handling complex semantic contexts and language variations. Therefore, further research is recommended to integrate language model-based approaches to improve the system's accuracy and generalization on real-world data.

The rapid development of Artificial Intelligence (AI) has brought significant changes to the criminal justice system, particularly in criminal investigations and evidentiary processes, while simultaneously raising complex legal and ethical challenges. Objective: This study aims to analyze the legal implications of the use of AI in criminal investigations, focusing on its benefits, risks, and challenges related to the admissibility of AI-based evidence, as well as the need for regulatory frameworks that ensure fairness, transparency, and accountability. Methods: This research employs a normative qualitative approach through the analysis of legal regulations, a review of legal and technological literature, and a comparative approach across jurisdictions, complemented by case studies of AI applications in law enforcement practices. Results: The findings indicate that AI enhances investigative efficiency through data analysis, crime prediction, and digital forensics; however, it also poses risks such as algorithmic bias, human rights violations, and issues concerning the reliability and transparency of evidence. Furthermore, differences across legal systems result in the absence of uniform standards for the admissibility of AI-based evidence. Therefore, adaptive regulatory frameworks grounded in the principles of fairness, transparency, and accountability are required, along with strengthened human oversight to ensure that the use of AI aligns with the principles of justice and human rights protection.

The development of Artificial Intelligence (AI) integrated with biometric technology has opened new opportunities in leadership development, particularly in enhancing emotional regulation capabilities within high-stakes environments. This study aims to analyze the role of AI-driven biometric feedback in improving self-awareness, emotional regulation, and decision-making quality among leaders. The study employs a systematic literature review approach by examining recent reputable scientific publications related to AI, biometrics, and leadership. The findings indicate that physiological data such as heart rate variability (HRV) and galvanic skin response (GSR), when processed through AI systems, can provide real-time feedback that enhances individuals’ ability to recognize and regulate emotions adaptively. Furthermore, the integration of this technology contributes to improved accuracy and consistency in decision-making under pressure. The results also reveal that the effectiveness of implementation is influenced by both technical and non-technical factors, including data quality, algorithm accuracy, and user acceptance. This study contributes to strengthening the integration of psychological and technological approaches in modern leadership research and offers practical implications for developing data-driven leadership training programs in the digital era.

This research aims to classify students’ lifestyles using the K-Nearest Neighbors (KNN) algorithm. The dataset consists of 392 high school students obtained from Kaggle, with key attributes including study hours, social media usage, Netflix viewing duration, attendance, sleep quality, internet quality, mental health, and extracurricular activities. KNN was chosen for its simplicity in distance-based classification, measured using Euclidean Distance. The data was divided into training and testing sets, then evaluated using accuracy and a confusion matrix. The results show that KNN effectively classifies students’ lifestyles into four categories: healthy, less active, at risk, and highly at risk. This classification is expected to assist educational institutions, parents, and students in understanding lifestyle patterns and their impact on academic performance and mental well-being. Furthermore, this study emphasizes the relevance of applying machine learning in education, aligned with Islamic values concerning health, discipline, and the optimal use of time.