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The development of social media, particularly TikTok, has played a significant role in shaping beauty trends and constructing ideal facial standards among young generations. The TikTok Beauty phenomenon not only provides diverse makeup references but also reinforces specific visual representations that influence perceptions of beauty. This study aims to examine the relationship between the TikTok Beauty phenomenon, the process of facial standardization, and its implications for the development of makeup techniques. A qualitative approach was employed through content analysis and literature review. Primary data were obtained from observations of popular TikTok videos under the hashtags #beautytrend, #makeup, and #tiktokbeauty, while secondary data were drawn from scholarly literature on beauty standards, social construction, and social media algorithms. The findings indicate that TikTok strengthens certain beauty standards such as fair skin, slim facial contours, and a flawless appearance which in turn influence techniques like complexion layering and visual manipulation. However, the platform also provides space for more inclusive and diverse beauty narratives. This study recommends enhancing aesthetic literacy to ensure that makeup practices prioritize diversity and the unique characteristics of each individual.

The increasing complexity of urban transportation systems demands intelligent and measurable navigation methods. Medan City, the capital of North Sumatra Province, has a dense road network with multiple route options that often confuse road users. Dijkstra's Algorithm, developed by Edsger Wybe Dijkstra in 1959, is a greedy-based computational approach proven effective for solving the shortest path problem on non-negative weighted graphs. This study applies Dijkstra's Algorithm to determine the shortest route from Medan Railway Station to Universitas Negeri Medan (UNIMED). The road network was modeled as an undirected weighted graph with 15 nodes and 16 edges, where edge weights represent actual road distances measured via Google Maps. The graph has a density of 0.152, confirming its sparse graph characteristic. Three alternative routes were identified and analyzed. The algorithm was implemented in Python 3 using the heapq module as a priority queue. Results show that the optimal route is A → B → C → E → F → M → N → O via Jl. M.T. Haryono, Jl. Aipda KS Tubun, Jl. Madong Lubis, and Jl. Prof. H.M. Yamin, with a total distance of 6.64 km. This achieves 99.1% accuracy compared to Google Maps, with a deviation of only 0.06 km. The optimal route is 6.25% more efficient than Alternative Route 1 (7.30 km) and 11.9% more efficient than Alternative Route 2 (7.54 km). The algorithm executes in under 1 millisecond with time complexity O((V+E) log V). These findings confirm Dijkstra's Algorithm as highly effective for medium-scale urban road network optimization.

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.

The digital transformation driven by the acceleration of Artificial Intelligence (AI) has fundamentally transformed leadership paradigms and the structure of the modern workplace. This study aims to provide an in-depth analysis of the various strategic opportunities and systemic risks arising from the integration of AI into contemporary organizational leadership practices, where technology is no longer merely a tool but a collaborative partner in managerial processes. Using a Systematic Literature Review (SLR) method on 30 reputable scientific journals with publications spanning up to 2026, this article analyzes the transition of the leader’s role from a conventional hierarchical model toward the concept of Augmented Leadership. Data analysis results indicate that AI implementation offers significant opportunities through data-driven decision-making systems with high precision, intelligent resource allocation optimization, and enhanced leadership intervention effectiveness, which has been documented to reach up to 35%. However, this transformation also brings a series of complex multidimensional risks, including algorithmic biases that could trigger inequality cascades, ethical dilemmas related to operational transparency (black box), and the potential erosion of the meaning of work for employees. The discussion in this article emphasizes that the success of this transformation depends not only on technical expertise, but also on redefining leadership competencies to prioritize emotional intelligence and ethical governance frameworks such as the CARE Framework (Control, Awareness, Responsibility, and Evaluation).

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 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.

Optimization problems in computer science often arise when a system must select the best combination from several alternatives under limited resources such as capacity, time, or cost. One commonly used optimization model is the Knapsack Problem, which involves selecting a number of items with specific weights and values to obtain the maximum profit without exceeding the available capacity. This study aims to analyze and compare the performance of the Greedy algorithm and Dynamic Programming in solving the 0–1 Knapsack Problem. The research employs a quantitative experimental approach by implementing both algorithms in a computer program and testing them on several datasets with different sizes. The evaluation parameters include the maximum value obtained and the algorithm execution time. The results show that the Greedy algorithm has faster execution time and more efficient memory usage, but it does not always produce an optimal solution. In contrast, the Dynamic Programming algorithm consistently produces an optimal solution, although it requires greater computational time. Therefore, the choice of algorithm should be adjusted to system requirements, whether prioritizing computational efficiency or optimal solution quality.

The issue of uninhabitable houses still requires an accurate identification mechanism because the manual data collection process has the potential to be time-consuming, costly, and subject to subjectivity in determining aid priorities. This study aims to develop a classification model to identify habitable and uninhabitable houses based on family socioeconomic data using the Random Forest algorithm. The research method includes data preprocessing, data division using stratified split in three scenarios, baseline model development, and optimization through hyperparameter tuning using GridSearchCV with 3-fold cross-validation and balanced class_weight parameters. The data used includes variables such as education type, employment status, occupation type, number of family members, and family insurance type. The test results show that the 70:30 data division scenario after tuning provides the best performance with a recall value of 0.5797 for uninhabitable houses and an F1-score of 0.4746. Feature importance analysis shows that education type and employment status are the most influential variables in the classification. The results of this study show that the model built is capable of increasing sensitivity in detecting uninhabitable houses to support more objective field survey prioritization.

This research explores the role of human algorithm interaction mechanisms in enhancing trust, reliability, and user confidence in Decision Support Systems (DSS). Traditional DSS models often focus solely on algorithmic accuracy and performance, neglecting crucial factors such as transparency and user engagement, which are essential for building trust. By incorporating explainable AI (XAI) techniques like SHAP and LIME, real-time feedback mechanisms, and user-friendly interfaces, the study develops structured interaction models that improve the interpretability of AI-driven decisions. The results show that transparent decision-making processes and interactive features significantly enhance user trust, making DSS more reliable and easier to adopt. Users interacting with systems that provide clear, understandable explanations of decisions, along with real-time updates on the system’s confidence, reported higher levels of decision-making confidence, especially in high-stakes scenarios. These improvements lead to greater user engagement and adoption of the system in various domains, including healthcare and finance. The study also highlights the importance of balancing interpretability with efficiency in user interface design to ensure both trust and usability. The findings contribute to the design of more user-centric DSS that prioritize trust, interpretability, and cognitive factors, providing a framework for the successful integration of intelligent decision support systems in complex decision-making environments. Future research should focus on refining interaction models and exploring the broader applicability of these systems in different sectors.

In high-performance computing (HPC) environments, the need to balance memory efficiency and query performance is crucial for ensuring optimal system performance. Traditional data structures, such as B-trees and hash tables, often prioritize either memory usage or query speed, leading to suboptimal performance in memory-constrained systems. This paper proposes a hybrid data structure that combines the strengths of multiple traditional data structures to optimize both memory usage and query processing speed. The proposed hybrid structure integrates cache-conscious algorithms, dynamic memory allocation, and compression techniques for intermediate query results. The approach is evaluated through extensive benchmarking tests comparing it to standard data structures like B-trees and hash tables under various workloads. Results show that the hybrid data structure reduces memory overhead by up to 30% while maintaining query processing speeds up to 1.5 times faster than conventional methods. Furthermore, the hybrid structure demonstrates robust performance across different types of queries, including both point and range queries, ensuring versatility and efficiency. The findings indicate that this hybrid approach provides a promising solution for HPC systems, where both memory efficiency and query speed are essential. Future research can explore extending the hybrid structure to distributed systems and emerging technologies, further improving its scalability and adaptability to new computational paradigms.

The rapid development of smartphone technology today creates challenges for consumers and manufacturers in determining an objective price range based on highly varied technical specifications. This study aims to implement the Random Forest algorithm in classifying smartphone price ranges into four main categories, namely low, mid-range, high, and flagship. The research method was carried out systematically through the stages of loading a dataset of 2,000 entries, exploratory data analysis (EDA) to ensure data integrity, and model training with a training and testing data split of 80:20. The results showed that the Random Forest model achieved a significant overall accuracy rate of 89%. Based on feature importance analysis, it was found that RAM capacity was the most dominant determining factor, contributing 47% to prediction accuracy, followed by battery power and screen resolution as supporting features. These findings have strategic implications for manufacturers to prioritize memory capacity upgrades in determining product pricing in the market, as well as providing guidance for consumers in assessing the fairness of a device's price based on its technical capabilities.

Budget efficiency is an important issue in state financial management because it is directly related to government spending priorities and their impact on public service programs. Discussions about budget efficiency policies are widespread on social media platform X, generating diverse public responses, thus necessitating an automated approach to understand public opinion trends more quickly and objectively. This research aims to analyze the sentiment of Indonesian people toward budget efficiency policies and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying sentiment. The research data used 10,909 Indonesian-language tweets sourced from a public dataset, which were then processed thru the preprocessing stages including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling is performed automatically using the Indonesian Sentiment Lexicon (InSet) approach to categorize data into positive, negative, and neutral sentiments. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), and then the data was divided into training and testing sets with an 80:20 ratio. Model performance evaluation was conducted using a confusion matrix and the metrics of accuracy, precision, recall, and F1-score. The research results show that sentiment distribution is dominated by negative sentiment at 56.78%, followed by positive sentiment at 37.40%, and neutral sentiment at 5.83%. In the classification stage, SVM performed best with an accuracy of 86%, while Naïve Bayes achieved an accuracy of 74%. These findings indicate that SVM is more optimal for sentiment classification on social media text data and can be utilized to more effectively support the analysis of public response to budget efficiency policies.

The disparity in the quality of rehabilitation services across regional work units presents a significant challenge to effective public management. This study aims to bridge the gap between problem diagnosis and policy prediction by proposing a hybrid, data-driven approach. We integrate K-Means Clustering to map the current state of service quality and Stochastic Simulation to predict the impact of strategic interventions. Using the 2024 Public Satisfaction Index (IKM) dataset from the National Narcotics Agency (BNN), the K-Means algorithm initially identified 26 work units (15.7%) in the "Red Zone" (critical performance), highlighting urgent areas for improvement. Next, a stochastic simulation modeling a "Directed Priority Intervention" scenario was run. The results predicted a significant structural shift in the distribution of service quality, characterized by an 80.8% decrease in critical units (down to 5 units) and a 71.8% increase in excellent performing units (up to 67 units). These findings validate that the integration of clustering and simulation provides a comprehensive framework for evidence-based decision-making, enabling policymakers to optimize resource allocation and efficiently accelerate national service standardization.

This study examines the inconsistency between the messages and behaviors of online preachers from Generation Z within the perspective of Islamic education. In the digital era, social media platforms such as TikTok, YouTube, and Instagram have become primary spaces for modern da’wah that are interactive and appealing to younger audiences. However, this advancement also presents significant challenges, particularly the dissonance between the Islamic values conveyed and the preacher’s actual behavior in the public sphere. This research employs a library research method by analyzing theories of communication, digital media, and Islamic education. The findings indicate that the main factors contributing to this inconsistency include algorithmic pressure from social media, commercialization of da’wah, lack of religious mentorship, and the digital culture of Gen Z that prioritizes popularity. From the standpoint of Islamic education, this phenomenon underscores the importance of integrating digital literacy, online preaching ethics, and character formation into both formal and non-formal educational curricula. Therefore, this study recommends strengthening the role of Islamic educational institutions in guiding young preachers so that digital da’wah remains ethical, educational, and truly reflective of authentic Islamic moral and spiritual values.

In the era of globalization, university graduates are required to have the ability to implement knowledge in real practice, which is realized through the Field Work program. This study aims to evaluate the compliance of Task Force reporting at PT PLN (Persero) North Sumatra Main Distribution Unit (UID North Sumatra). The method used is a quantitative approach with K-Means Clustering. Compliance reporting data was obtained from internal company documents, which then went through the preprocessing and clustering stages using the K-Means algorithm, with the determination of the optimal cluster number through the Elbow and Silhouette methods. The K-Means clustering analysis results identified two groups of units with different levels of compliance. Cluster 2, consisting of UP3 Binjai and UP3 Sibolga, showed a higher and more consistent level of reporting compliance. In contrast, Cluster 1 (including UP2D, UP3 B. Barisan, UP3 L. Pakam, UP3 Medan, UP3 Medan Utara, UP3 Nias, UP3 P. Sidimpuan, UP3 P. Siantar, and UP3 Prapat) had a tendency for lower compliance. This finding indicates a difference in reporting consistency that affects the effectiveness of work safety supervision. The K-Means method is proven to help PLN management identify units with low compliance, allowing corrective actions to be prioritized appropriately.

CPU scheduling is a core function in modern operating systems that significantly impacts system performance and efficiency. Among various scheduling algorithms, Priority Scheduling is widely used and exists in two main variants: non-preemptive and preemptive. The non-preemptive mode allows a process to run to completion, while the preemptive mode can interrupt a running process for a higher-priority one. Understanding the behavioral differences between these modes is crucial but often challenging through manual calculations. To address this, an interactive web-based application was developed to simulate and visualize both preemptive and non-preemptive Priority Scheduling algorithms. The research method involved designing the system logic based on the core principles of each scheduling variant, followed by implementation using standard web technologies: HTML, Tailwind CSS, and JavaScript. The application allows users to input custom process data or load predefined case studies, select the scheduling mode, and instantly receive a comprehensive analysis. The results include a dynamically generated Gantt chart, a detailed performance metrics table (including turnaround time and waiting time), and a step-by-step execution log. Through a comparative analysis of a specific case study, the application is proven to be an effective educational tool. It accurately simulates both modes and visually demonstrates the impact of preemption on execution order, resource utilization, and key performance metrics, thereby simplifying the learning process for students and educators.

Financial management in hospitals is a crucial aspect to ensure the sustainability of quality health services. However, the complexity of financial data, which involves various budget components, often creates challenges for hospital management in conducting accurate analysis and budget planning. Therefore, a data-driven approach is required to present financial information in a structured and comprehensible manner. This study examines the application of the K-Means Clustering method to classify hospital financial data based on expenditure characteristics and patterns, with a case study at RSUD Rantau Prapat as part of a community service program. The financial data were analyzed through pre-processing stages, determination of the optimal number of clusters using the Elbow Method, and the implementation of the K-Means algorithm to generate more representative budget groups. The results indicate that clustering hospital financial data into three main categories—routine operational costs, medical service costs, and administrative/personnel costs—provides clearer insights into budget distribution. This supports hospital management in identifying budget allocation priorities, detecting potential inefficiencies, and improving the overall efficiency of financial governance. The limitation of this study lies in the data scope, which only involved a single hospital, thus restricting its generalizability. Future research is recommended to expand the scope to multiple hospitals and integrate alternative clustering methods to obtain more comprehensive results.

The study presents a comprehensive framework for optimizing customer retention budget by integrating clustering, classification, and mathematical optimization techniques. The study begins with the IBM Telco dataset, which is prepared through data cleansing, encoding, and scaling.  In the preliminary phase, customer segmentation is performed using K-Means clustering, with k = 3 and k = 4 identified as optimal based on the elbow method and Silhouette score. The configurations produced three (Premium, Standard, Low) and four (Premium, Standard Plus, Standard, Low) customer segments based on purchase preferences, which served as input features for churn prediction. In the second phase, the dataset was divided into training and test sets in an 80:20 ratio, followed by data balancing using the Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbors (ENN). Multiple classification algorithms were evaluated, including Naive Bayes (NB), Random Forest (RF), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Gradient Boosting (GB), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) using F1-score as the performance metric. CatBoost and LightGBM, with k values of 3 and 4, respectively, were the highest-performing classification models, with only minimal differences in performance.    Ultimately, customer segmentation established customer prioritization, whereas churn prediction assessed customer churn likelihood. Four distinct configurations were assessed utilizing mixed-integer linear programming (MILP) to optimise retention budget allocation within uniform budget constraints, discount amounts, and churn thresholds. In both the k=3 and k=4 scenarios, CatBoost surpassed LightGBM, with CatBoost at K=3 effectively discounting 66% of at-risk consumers across all three segments, hence improving the intervention's efficacy and budget allocation, making it the ideal choice for maximizing customer retention. The results demonstrate the importance of segmentation in enhancing retention budgeting and budget optimization, particularly concerning parameter sensitivity.

Pendistribusian bantuan yang efisien menjadi tantangan utama dalam proses penyaluran. Penelitian ini bertujuan untuk merancang sistem cerdas yang mampu memetakan dan menyalurkan bantuan secara optimal dengan pendekatan algoritmik. Metodologi yang digunakan meliputi pemodelan sistem dengan menggunakan Unified Modeling Language (UML), perancangan struktur basis data relasional, dan perancangan algoritma penyaluran berdasarkan kriteria prioritas dan efisiensi logistik. UML digunakan untuk menggambarkan arsitektur sistem secara visual, meliputi use case, class, dan diagram aktivitas. Perancangan basis data dilakukan untuk memastikan integritas data dan kemudahan pengelolaan informasi bantuan, lokasi, dan kebutuhan penerima. Algoritma yang dikembangkan memanfaatkan pendekatan heuristik untuk menentukan rute penyaluran dan prioritas penerima berdasarkan parameter lokasi geografis. Hasil penelitian ini berupa prototipe model konseptual yang dapat digunakan sebagai dasar pengembangan sistem cerdas berbasis teknologi untuk mendukung proses penyaluran bantuan yang adaptif dan responsif.

Technological advancements have brought fundamental changes in the way we interact with digital images and photography. One significant milestone in this development is the Photoshop Express Photo Editor, which has become a primary platform for image processing and editing. Datasets are used to analyze sentiment and are utilized during the accuracy testing phase. Based on the testing results, the Convolutional Neural Network (CNN) algorithm achieved an average accuracy value of 86.50%, compared to the Naïve Bayes (NB) algorithm, which achieved an average accuracy value of 75%. The results of the research conclude that the choice of sentiment analysis method should be tailored to the needs and limitations of the system. If a fast, light, and easy-to-understand process is required, the Naive Bayes method is the right choice. However, if accuracy and context understanding are the top priorities, then CNN is a superior approach, although it requires more resources. Additionally, based on the Wordcloud data, it is known that the majority of comments are positive, indicating that the reviews or texts analyzed contain many positive expressions related to quality, usability, and ease of use.