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The policy of shutting down TikTok Shop has sparked both pros and cons. On one side, it eliminates jobs for content creators whose income relies on TikTok Shop, while on the other side, it saves UMKM  from predatory pricing wars that harm them. Utilizing the Naive Bayes algorithm, a classification method capable of predicting the likelihood of a class and making decisions based on learning data, the Emotion Recognition research on YouTube comments related to the closure of TikTok Shop is conducted. Data will be classified into five classes: happy, angry, sad, afraid, and surprised. The objective of this research is to find the best emotional model using the Naive Bayes method. The results of user testing with Naive Bayes and Tf-Idf show that the precision values for sad, happy, afraid, and surprised emotions are high, while for anger, the percentage is 59%. The percentages for afraid, happy, sad, and surprised emotions are 91%, 87%, 84%, and 79%, respectively. The overall accuracy is 82%.

During the period of compulsory education in Indonesia, which is 12 years, the government requires mathematics as the main subject of external learning which will continue to have an impact on people's lives. The level of ability in a field of science must be measured to obtain valid data for the development of education in Indonesia. The use of Sagemath Mathematics Software in schools in Indonesia is not surprising at this time, technological developments have made the use of Sagemath Mathematics Software commonplace. The research method used in this research uses a literature study research method where researchers research the results of previous research. In groups There were 55.88% of experiments that experienced an increase (normalized gain) in the high category and 44.22% were in the medium category. Meanwhile, the average increase (normalized profit) in this group was 0.71, which is included in the high category. The results of this study concluded that students' abilities in mathematical computational thinking on Sagemath software are very high, this is due to several factors between the tendencies of students' desires and creativity of the teaching profession. Students' computing abilities when using Sagemath software can be done through students' mathematical algorithm features.    

The primary problem for multi-robot displacement and motion phase solving requires that the robots prevent themselves from colliding with each other as well as stationary obstacles. In certain situations, robot conflict is unavoidable if one robot views its neighbors as immovable obstacles. Hence, this paper proposes a new NURBs (Non-Uniform Rational B-spline) based algorithm for multi-robot path planning in a crowded environment. First, the proposed technique finds each robot's free, smooth, optimal path while avoiding collision with the existing obstacles. Secondly, the prospect of possible collision between the preplanned trajectories will be computed to allow the robots to navigate in the same workspace and coordinate between them. Then, each robot's time to arrive at potential collision sites is computed based on its speed. As a result, the robots involved in the collision must choose whether to use the robot priority technique to prevent the collision. Simulation results under different scenarios and comparisons with previous works are provided to validate the work. The obtained results prove that the proposed approach is accurate (as the robot's instantaneous speed is taken into consideration), fast (as there is no need to broadcast the robots’ positions), the robots’ paths are optimal and smooth (to avoid jerk movements), and the approach ensures that the robots will not be trapped by local minima problem.

This journal reviews the use of Cisco simulators in building and analyzing ring topology networks and the routing protocols used in them. Ring topology is one of the commonly used network topologies where all nodes are directly connected to form a circle. The routing protocol in ring topology uses Bellman Ford and Dijkstra algorithms to determine the best path. The main focus of this research is on Enhanced Interior Gateway Routing Protocol (EIGRP), a routing protocol developed from Interior Gateway Routing Protocol (IGRP). EIGRP enables fast convergence and efficiency in data transmission in the network. The research methods used are literature and simulation methods using Cisco Packet Tracer software.    

This paper explores the use of genetic algorithms (GAs) for optimizing nonlinear systems in resource allocation. By simulating various allocation scenarios, we demonstrate the efficiency of GAs in finding near-optimal solutions in complex environments. The study provides a comparison of GA performance against traditional optimization methods and identifies scenarios where GAs outperform. The results emphasize the utility of GAs in real-world applications, especially when conventional approaches struggle with large solution spaces.

This study aims to analyze purchasing patterns in online transactions using the Apriori algorithm to support sales strategy optimization. The research was conducted on transactional data from an online store selling household and daily-use products. The Apriori method was applied to identify associations between items based on minimum support and confidence thresholds. Four experimental scenarios were tested to compare the reliability of generated rules and determine the strongest item relationships. Data preprocessing included item grouping, transaction coding, and elimination of non-frequent items. The results show several strong association rules with lift ratio values above 1, indicating meaningful item relationships. The strongest rule identified was the association between forks and spoons, forming a highly relevant combination for product bundling strategies. The findings demonstrate that the Apriori algorithm can assist online stores in planning stock, designing product bundling, and improving marketing effectiveness. The research contributes practical insights for business decision-making and highlights the significance of data mining in e-commerce environments.

The rapid growth of data-intensive applications has posed significant challenges for classical machine learning (ML) algorithms, particularly in terms of computational efficiency and scalability. This study explores the role of quantum computing in optimizing machine learning performance through the implementation of Quantum Machine Learning (QML), specifically using the Quantum Support Vector Machine (QSVM) model. The research adopts a Design Science Research approach, involving problem identification, model development, system implementation, and performance evaluation. Both classical Support Vector Machine (SVM) and QSVM models are developed and tested using benchmark classification datasets. The results indicate that QSVM outperforms the classical SVM model across multiple evaluation metrics, including accuracy, precision, recall, and F1-score. Additionally, QSVM demonstrates improved computational efficiency by reducing training time, particularly when handling high-dimensional data. These improvements are attributed to the ability of quantum computing to utilize quantum kernel methods and map data into higher-dimensional feature spaces, enabling better pattern recognition and classification performance.  Despite these promising outcomes, the study also identifies several limitations related to current quantum hardware, such as noise, decoherence, and limited qubit availability, which may affect scalability and practical implementation. Therefore, further research is required to enhance quantum hardware reliability and develop hybrid quantum-classical models. In conclusion, quantum machine learning offers a promising solution to overcome the limitations of classical approaches, providing enhanced performance and efficiency for complex data processing tasks in future intelligent systems.

This study presents an advanced structural health monitoring (SHM) system for steel bridges based on wireless sensor networks (WSN) integrated with machine learning algorithms. The proposed system monitors and predicts structural integrity under various load conditions. The research focuses on developing a machine learning model capable of real-time anomaly detection, allowing for early warnings of potential failures. Experimental results from both simulation and field tests demonstrate the system’s effectiveness in prolonging bridge lifespan while reducing maintenance costs.

The gaming industry continues to grow rapidly, with various genres having different sales patterns. This research aims to group the best game genres based on sales patterns using the K-Means Medoid algorithm. This method was chosen because of its ability to overcome data diversity and reduce the influence of outliers compared to the conventional K-Means algorithm. The data used in this research includes game sales information from various platforms, which is then analyzed to find distribution patterns and market trends. The results of this research show that certain game genres have superior sales patterns compared to others, and there are special characteristics in the groups that are formed. These findings are expected to help game developers and publishers in making strategic decisions regarding the development and marketing of their products. In addition, this research also contributes to the development of a data-based recommendation system that can be used to understand market preferences more accurately.

This paper presents a comparative analysis of various battery management systems (BMS) in electric vehicles, with a focus on incorporating machine learning techniques to improve battery safety and extend battery life. The study evaluates conventional BMS against machine learning-enhanced models in predicting thermal runaway, state of charge (SOC), and state of health (SOH) under diverse operating conditions. Results indicate that machine learning algorithms outperform conventional methods, providing more accurate SOC and SOH estimations, thus enhancing vehicle safety and longevity.

This study explores the integration of predictive analytics into supply chain management within national e-commerce enterprises. Predictive analytics, which utilizes historical data combined with machine learning algorithms, regression analysis, and time series forecasting, has shown significant improvements in operational efficiency. The study focuses on four key areas: demand forecasting, inventory management, transportation optimization, and customer satisfaction. By predicting demand more accurately, e-commerce platforms can reduce stockouts and overstock situations, streamline logistics routes, and lower logistics costs. The implementation of predictive analytics led to a 20% reduction in delivery times and a 15% decrease in logistics costs, thereby enhancing customer satisfaction. However, the study also highlights challenges in integrating real-time data from multiple sources and scaling predictive models across diverse product categories and geographic regions. The results emphasize the need for e-commerce platforms to invest in technology that enables seamless data integration and the development of region-specific predictive models. The findings are compared with industry benchmarks, showing that the improvements in logistics and supply chain performance align with global trends. Based on these results, the study recommends best practices for implementing predictive analytics, including effective data collection, machine learning model training, and scalability considerations. By following these practices, e-commerce companies can optimize their supply chains, reduce operational costs, and increase customer satisfaction, positioning them for greater competitive advantage in the marketplace.

Cloud-based resource allocation and VM/container orchestration play a crucial role in ensuring performance, scalability, and energy efficiency in modern distributed computing environments. This study investigates the effectiveness of centralized and decentralized scheduling models combined with heuristic and optimization-based allocation strategies in container-based cloud infrastructures. A quantitative experimental approach was employed to evaluate system performance under varying workload intensities. Key evaluation metrics included response time, throughput, resource utilization, SLA violation rate, and energy consumption. The experimental results indicate that centralized scheduling mechanisms experience scalability limitations and increased latency under high workload conditions. Although optimization-based allocation improves performance within centralized architectures, coordination bottlenecks remain significant. In contrast, decentralized scheduling models demonstrate superior adaptability, reduced response time, and improved throughput due to distributed decision-making and reduced control overhead. The integration of intelligent optimization techniques further enhances resource utilization and energy efficiency, achieving the lowest SLA violation rates and highest system stability. Overall, the findings confirm that combining decentralized scheduling with optimization-driven resource allocation provides a more scalable and sustainable orchestration strategy for modern cloud environments. This approach is particularly suitable for dynamic, large-scale, and latency-sensitive applications in container-based and edge-integrated cloud systems.

This systematic literature review examines the evolving role of Quality Engineering (QE) in optimizing production processes within Industry 4.0 contexts. By analyzing 78 peer-reviewed studies (2010–2025), the research identifies critical shifts in QE methodologies, emphasizing integration with artificial intelligence (AI), machine learning (ML), and real-time digital twin technologies. Key findings reveal enhanced robustness through adaptive optimization algorithms (e.g., Bayesian optimization, NSGA-II), improved defect prediction via AI-driven quality control systems, and streamlined process interoperability through Manufacturing Execution Systems (MES) and Quality 4.0 frameworks. The study underscores digital integration as a catalyst for reducing variability, accelerating decision-making, and aligning quality management with Industry 4.0’s demands for agility and interconnected systems. Recommendations include adopting hybrid methodologies combining classical Six Sigma with ML-driven analytics and investing in workforce training for digital QMS adoption.

Obtaining high-quality, diverse, accurate datasets for sentiment analysis has always been a significant challenge. Traditional approaches include annotators, which may introduce bias to datasets and are also time-consuming and expensive. These types of datasets may also not represent the variety needed to train robust and generalizable sentiment analysis models. This study introduces a novel combination of techniques to approach the problem with a novel solution. The proposed system, SentiGEN includes the use of a transformer, T5, fine-tuned and optimized using an evolutionary algorithm to generate high-quality, diverse, accurate data for sentiment analysis. The generated data is validated using XLNet to ensure high sentiment accuracy. This combination of technologies has proven successful based on the results derived from evaluating multiple models. From complex transformers such as BERT to more straightforward approaches like KNN, those trained using synthetic data demonstrated superior performance compared to their counterparts trained on real data. This enhancement in predictive accuracy was observed when evaluated on benchmark datasets such as SST-2 and Yelp. SentiGEN can generate high-quality, diverse, accurate, realistic data for sentiment analysis and successfully increased the performance of models trained on synthetic data compared to the same model trained on real data.

Computational Thinking is a way of thinking that refers to the ability to solve problems using concepts used in computing. Computational thinking is a way of thinking in solving problems that students must develop in the digital era. This research aims to examine the importance of computational thinking in improving students' mathematical problem solving abilities. The type of research used is qualitative research using a literature study approach in the form of books, notes, journals, reports of relevant research results, as well as the results of observations. The results of the analysis show that it is very important for students to have computational thinking in order to improve their mathematical problem solving abilities. Computational thinking involves solving problems using a logical and systematic mindset that involves selecting and using algorithms, data representation, problem decomposition, abstraction, pattern recognition and hypothesis testing. Computational thinking has become an important and essential skill for humans in the 21st century. Educators must advocate the importance of integrating learning with computational thinking concepts into the educational curriculum. Computational thinking has an important role in learning. This can be useful for improving mathematical problem solving abilities. Apart from that, computational thinking can also develop critical, creative and analytical thinking skills in solving complex problems, both in the context of computing and everyday life.    

The aim of this research is to provide a study of the computational thinking process of mathematics learning. This research uses library research methods. The data obtained is the publication of research articles in scientific journals. Data analysis includes three stages: organize, synthesize, and identify The result of previous research is students' computational thinking abilities need to be improved in abstraction and algorithms.      

This research aims to analyze the effect of using Web Wordwall in training students' computational thinking skills in mathematics learning with a qualitative approach. The research subject is the Director General of Teachers and Education Personnel, while the object is education that is liberating and pro-student. Data obtained from online and offline information sources is recorded in a structured manner, then analyzed to be synthesized into a systematic conceptual framework. Conclusions are drawn by taking the essence of the conceptual framework that can answer the research questions posed. The research method used is a case study with a focus on participatory observation, in-depth interviews and document analysis. Data was collected through literature study and analysis of learning materials. Data analysis was carried out using an inductive approach, by looking for patterns of findings and main concepts from the data collected.

This research aims to analyze the effect of using Web Wordwall in training students' computational thinking skills in mathematics learning with a qualitative approach. The research subject is the Director General of Teachers and Education Personnel, while the object is education that is liberating and pro-student. Data obtained from online and offline information sources is recorded in a structured manner, then analyzed to be synthesized into a systematic conceptual framework. Conclusions are drawn by taking the essence of the conceptual framework that can answer the research questions posed. The research method used is a case study with a focus on participatory observation, in-depth interviews and document analysis. Data was collected through literature study and analysis of learning materials. Data analysis was carried out using an inductive approach, by looking for patterns of findings and main concepts from the data collected.

This study aims to find out how students apply the four foundations of computational thinking in solving mathematics problems and whether students have applied computational thinking in solving mathematics problems. The research was conducted using an analytical approach to examine the results of solving mathematics problems. This approach involves collecting data from various sources, including tests, quizzes, or assignments given to research subjects. The collected data was then analyzed comprehensively to identify the use of four foundations of computational thinking in the process of solving mathematical problems. The results of this research are that students apply the four foundations of computational thinking. Decomposition by writing down what is known and looking for what is needed in the problem. Pattern recognition by understanding what is known and asking then using the right formula. Abstraction by writing formulas, ignoring what is not needed in the problem, and simplifying the answer. Algorithm by entering the required values into the formula then calculating them and solving the problem in the correct sequence of steps. Even though students make mistakes when solving mathematics problems, students can apply the four foundations of computational thinking.    

Computational Thinking skills are students' ability to use mathematical concepts to solve problems systematically using algorithms and programming. This research aims to measure the development of Computational Thinking skills of students at SMA N 3 Medan on algebraic function limits material. The research method used is survey research with data collection using tests and observations. The subjects of this research were class XI students from SMA N 3 Medan who were studying algebraic function limits. The research results show that there is significant development in students' Computational Thinking skills after studying the material. The implication of this research is the need for a learning approach that focuses more on developing Computational Thinking skills in mathematics learning in high school.