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The era of Globalization has an impact on the acceleration of technology which affects business transaction media and buying and selling patterns of the community, this encourages people to use business transaction media using the internet in the form of sites and buying and selling application systems or more formally known as e-Commerce and triggers consumptive behavior, especially millennials and Gen Z, but the lack of literature makes a research gap found, namely the Research Evidence Gap. The method used in this research is quantitative by using the SPSS statistical software tool version 27, with a sample of 31 students at Politeknik STIA LAN Jakarta. Data is collected through a questionnaire containing questions about online shopping patterns, ease of access to use and payment on online shopping applications or websites, alternative choices of online shopping applications or websites, online shopping methods, frequency of purchases, factors that influence the emergence of consumptive behavior in students of the Politeknik STIA LAN Jakarta human resource management study program of the apparatus class of 2022. After statistical processing, the result of simple regression analysis is Y = ax + b or Y = 8.516 + 0.429, the T test (Hypothesis), namely T count worth 3.440 is greater than t table worth 2.04523, then H0 is rejected and Ha is accepted, it can be concluded that there is an influence of variable x (the influence of e-Commerce) on variable y (consumptive behavior) and the test of the coefficient of determination (R Square) is 0. 290 This value shows that the influence of the e-Commerce variable on the consumptive behavior of students is 29%, while the rest (71% of student consumptive behavior) is influenced by other variables.

This research is entitled "Analysis of Character Education Values ​​in the Ratoeh Jaroe Dance in Extracurricular Learning at SMA Negeri 3 Banda Aceh". This research raises the issue of the value of character education in the Ratoeh Jaroe Dance in extracurricular activities. This research aims to analyze and describe the value of Ratoeh Jaroe Dance character education in extracurricular activities. The type of research used in this research is descriptive with a qualitative approach. The results of the research show that there is value in character education found in various movements, verse, floor patterns, fashion and make-up. The value of religious education is found in the various movements (saleum, aamiin and sep-sep), while in the verse namely (saleum, aamiin, lamburak, hasan summa husein, aroh pulo pineung and sep-sep), the dress code is in accordance with Islamic law. The educational value of nationalism is found in the various movements of the jakku timang, while in the verse namely the jak hai tajak and in traditional clothing. The value of independent education is only found in the verse of Hasan Summa Husein. The value of mutual cooperation education is found in the various movements (jak hai tajak, kasep kapot, uleue-uleue beu mate, lamburak, peulot manok, aroh pulo pineung, and uroe), and in the verse namely uleue-uleue beu mate and uroe. The value of integrity education is contained in the verse (jakku timang, kasep kapot, and peulot manok), and is found in a dancer namely Syeh. 

Life in the modern era is characterized by rapid lifestyle changes, including the 'skena' trend that becomes an integral part of student culture at the University of Jember. This research aims to analyze the lifestyle trend "skena" in the socio-cultural framework in the modern era, with a focus on Jember University students as a case study. A qualitative approach is used to collect and analyze data through in-depth interviews and participatory observations. The results show that the 'skena' trend reflects patterns of behavior, values, and norms that develop in student society. Social and cultural factors such as technology, globalization, identity, and self-expression play an important role in shaping and influencing these trends. This research provides an in-depth understanding of the phenomenon of "skena" and its implications for student culture at the University of Jember and provides a foundation for further research on lifestyle trends in the socio-cultural context.

This research aims: (1) to describe the feasibility of e-jobsheets for making camisole patterns in costume made subjects at State Vocational Schools 1 Jabon, (2) to describe student learning outcomes regarding the development of e-jobsheets for making camisole patterns for costume made subjects at State Vocational Schools 1 Jabon. This research uses the research and development (RnD) research method. The results of this research state that (1) the level of feasibility of developing an e-jobsheet for making camisole patterns by media experts and material experts with an average score of 4.23 in the very feasible category, (2) student learning outcomes for developing an e-jobsheet for making camisole patterns The costume made subject obtained a percentage of 95% which was declared passed and 5% which was declared not passed.

This study aims to identify the influence of organizational culture, transformational leadership, and work motivation on the performance improvement of civil servants at the Main Secretariat of the National Civil Service Agency. Data analysis was conducted using Linear Structural Relationship (Lisrel) version 8.80 with Structural Equation Modeling (SEM) method. The choice of using SEM in this research lies in its ability to analyze the pattern of relationships between latent constructs and their indicators, latent constructs with each other, and measurement errors directly. The results of the analysis indicate that organizational culture, transformational leadership, and work motivation significantly contribute to the improvement of civil servants' performance. Managerial implications and recommendations for further research are also discussed.                                                                                                                                                      

Training is one of the functions that can be used to improve human resources. Training can be done in various educational institutions. In the world of education there are classes that are accepted not only students who have normal physical characteristics, but there are also learning schools that accept students who can be called Special Needs Children. Children with special needs can be said to be children who have limitations or differences compared to other normal children. These limitations can be shown through physical, ability, and emotional aspects. This research aims to analyze and suitable education patterns for children who experience special needs. The method used in this research is qualitative research using library research method by collecting data from recent articles. The results of this study show that children with special needs do not mean that they are not entitled to education like other normal children. Education for children with hearing and speech impairments can be done by implementing a learning environment that is tailored to their needs, for example: visual videos, picture cards, photos, and others.

This research examines the mapping of creative culinary industry clusters typical of Central Kalimantan in Palangka Raya City using Hierarchical Cluster Analysis. The main objective of this research is to identify clustering patterns of the local creative culinary industry which can be the basis for formulating local culinary optimization policies. By applying Hierarchical Cluster Analysis, this research succeeded in uncovering the characteristics and relationships between creative culinary industry players in Palangka Raya, as well as development potential that can be improved through appropriate policies. It is hoped that the results of this research can provide strategic recommendations for local governments and other stakeholders in designing and implementing policies that support the growth of the creative culinary industry, while promoting the richness of Central Kalimantan's unique culinary delights.

The objectives of this research include 1) To determine the application of the Project Based Learning model in making embroidery decoration designs in the fashion decoration subject to improve the learning outcomes of Class XI students at SMKN 1 Sooko. 2.) To determine the effectiveness of applying the Problem Based Learning model in creating embroidery decoration designs in the fashion decoration subject to improve the learning outcomes of Class XI students at SMKN 1 Sooko. This type of research is quantitative research with a pre-experimental design and a one shot case study model. This research was carried out at SMK Negeri 1 Sooko in class XI Design and the number of students was 22 people. The data collection method used is observation to determine the application of the Project Based Learning model to embroidery decoration designs, questionnaires to determine student learning responses and tests to determine the effectiveness of implementing learning models on student learning outcomes. The results of the research state that the application of the Project Based Learning learning model can be seen from the implementation of the Project Based Learning learning model in the design of embroidery decorations in the fashion decoration subject, obtaining an average score of 3.7 for teacher activities and an average score of 3.4 for student activities. Student learning outcomes on the cognitive test obtained an average score of 81.5 and the average score on the psychomotor test obtained an average score of 87.5. The overall average score of students' learning outcomes was 86.4, which was declared complete the Minimum Learning Standard. As many as 85% of students completed the Minimum Learning Standard and 15% did not completed.

This research adopts a qualitative approach with a literature review design, exploring the interaction between media and Public Relations (PR) during crisis situations. Utilizing secondary data sources such as journals and books, this study investigates the factors influencing the collaboration between media and PR, as well as effective communication strategies in crisis management. Data analysis identifies common patterns, themes, and trends emerging from the reviewed literature, providing a comprehensive understanding of the dynamics of the relationship between media and PR during crises. The research findings highlight the importance of transparency, prompt responsiveness, and solid collaboration between media and PR in effectively managing crises. The contribution of this study lies in deepening the understanding of the vital role of collaboration between media and PR in safeguarding the reputation and image of companies during crisis situations.

The scientific paper with the title arranging the song I want to follow Jesus in the quarted band ensemble playing in the GPdI Agave Bangun Service is research that discusses how to apply the branch of arrangement, namely reharmonization, to the composition I want to follow Jesus. This research uses qualitative research methods using observation, interviews and documentation. Arrangement comes from the Dutch word Arrangement, which means adjusting a musical composition to the number of singers' voices or musical instruments based on an existing composition so that the essence of the music does not change. The arrangements influence the formation of the rhythm and harmony produced by the quarted band ensemble at GPdI Agave Bangun. The results of the research show that the problem of this research is that the drum and bass, which are the main components in the quarted band ensemble, are out of sync between the two instruments which will affect the quality of the arrangement. The piano is often melodic but can also be an accompaniment instrument, the piano playing pattern becomes a rhythmic fulcrum that will change the character and dynamics of the song. Reharmonization of the piano chords is the main reason the writer arranged the song I want to follow Jesus by changing the character of the song from sad to straightforward. Substitution chord theory, voicing and slash chord techniques are the main ingredients for researchers in composing. The type of research used in this research is qualitative research, with the data collection techniques used are observation, interviews and documentation. The final result of this research is as reference material for church musicians to increase their repertoire of playing variations to accompany worship in the church.

This research aims to formulate a social forestry management strategy for HKm Sulian Besar in Keban Jati Village, Air Nipis District, South Bengkulu Regency. This research was carried out from July to August 2024 in Keban Jati Village, Air Nipis District, South Bengkulu Regency, Bengkulu Province. The research location was carried out in the Sulian Besar Community Forest. This research uses a combination method (mixed) dominantly qualitative and less dominantly quantitative. To recommend strategies for community forest management (HKm) that have achieved economic, social and ecological and institutional benefits by identifying using SWOT analysis to analyze various factors systematically. The position of the community forest management strategy in HKm Sulian Besar, Keban Jati Village, Air Nipis District, South Bengkulu Regency is in quadrant III, namely the turn around strategy where the implementation of the social forestry program of the community forest scheme has great opportunities but there are internal weaknesses so you have to choose the right strategy so that weaknesses are not reduce the odds. Alternative strategies that can be implemented include restructuring group institutions, increasing institutional, technical and marketing capacity, improving cultivation patterns and conducting effective NTFP marketing.

This article presents how the factors affecting family owned and managed businesses are examined and related to how the business literature develops. In addition, it discusses important patterns in the current literature and new directions regarding family business for future study. This study identifies the main research topics and methodical approaches in a systematic literature review to understand the family business. Insights about behind the historic changes and current directions and trends of family business literature are also identified. The findings of this study argue that the main cause of challenges in running a family business is sourced from managing the relationship between family interests and business interests. The key success factors are strong leadership and building a culture that accepts continuous change.

Obesity is a major cause of diseases such as diabetes, heart disease, and other metabolic disorders. A preliminary study conducted on 15 police officers from the Central Kalimantan Police Department revealed that 9 officers (60%) were obese, while 6 officers (40%) were not. Among the 9 obese officers, 8 admitted to rarely engaging in regular physical activity and frequently consuming fast food, while 1 officer was in the process of losing weight by exercising and maintaining a healthy diet. The causes of obesity include physical activity and dietary patterns. The objective of this study was to analyze the relationship between physical activity and dietary patterns and the incidence of obesity among police officers at the Central Kalimantan Police Department. Methods: This correlational study used a cross-sectional approach to examine the relationship between physical activity and dietary patterns and the incidence of obesity among police officers at the Central Kalimantan Police Department. Data collection instruments included questionnaires and BMI measurements. The sample consisted of 196 police officers classified as overweight and obese, selected through simple random sampling. The data were analyzed using the Chi-Square correlation test. Results: The study found a significant relationship between physical activity (p-value = 0.000, 83.6%) and dietary patterns (p-value = 0.000, 73.8%) and the incidence of obesity among police officers at the Central Kalimantan Police Department. Conclusion: Physical activity and dietary patterns are significantly related to the incidence of obesity among police officers at the Central Kalimantan Police Department. The role of nurses in addressing obesity should begin with an accurate assessment of family care patterns, ensuring that interventions and education provided are effective, and prioritizing promotive and preventive measures to address obesity and prevent the emergence of chronic diseases.

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 research aims to explore the potential of social media in supporting the implementation of public information disclosure policies. This study employs a qualitative approach with a case study method. Data is collected through in-depth interviews with relevant stakeholders, participant observation, and social media content analysis. The collected data is analyzed using thematic analysis techniques to identify patterns, themes, and insights relevant to the research objectives. Results This research reveals that social media has significant potential in strengthening the implementation of public information disclosure policies. Social media can facilitate wide dissemination of information, enhance public participation, and promote government transparency and accountability. However, challenges such as digital literacy, regulations, and adequate policies must be addressed to fully utilize the potential of social media.

This study compares different machine learning models for time series forecasting in financial data analysis. Models including ARIMA, LSTM, and GRU are applied to predict stock price movements. We measure the accuracy and computational efficiency of each model on various datasets and discuss their strengths and weaknesses in financial forecasting contexts. The findings suggest that deep learning models show significant improvement in capturing complex temporal patterns over traditional methods.

The rapid growth of digital technologies has significantly increased the complexity and frequency of cyber threats, making network security a critical concern in modern information systems. Traditional security approaches, such as rule-based and signature-based systems, are often limited in detecting sophisticated and unknown attacks. Therefore, this study proposes an Anomaly-Based Intrusion Detection System (AbIDS) utilizing machine learning and deep learning techniques to enhance detection capabilities. The research adopts a Design Science Research approach, involving stages of problem identification, data collection, preprocessing, model development, system implementation, and evaluation. Several models, including Decision Tree (DT), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are implemented and compared. The results indicate that deep learning models, particularly LSTM and CNN, outperform traditional machine learning methods in terms of accuracy, precision, recall, and F1-score, while maintaining a lower false positive rate. Additionally, the integration of incremental learning enables the system to adapt to new attack patterns without requiring complete retraining, improving scalability and real-time performance. Despite the promising results, challenges such as computational complexity and false positives remain. Overall, the proposed IDS model demonstrates strong potential as an effective and adaptive solution for enhancing network security in dynamic 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 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.

This study explores the development and evaluation of an adaptive Intrusion Detection and Response System (IDRS) driven by Reinforcement Learning (RL) for securing 5G networks. The RL-based IDS is designed to overcome the limitations of traditional security systems by dynamically learning from real time network traffic and adapting to emerging cyber threats. Introduction: The rapid growth of 5G networks, with their increased number of connected devices and complex traffic patterns, necessitates advanced security solutions that can detect and respond to evolving cyberattacks. Literature Review: Traditional Intrusion Detection Systems (IDS), including signature based and anomaly based methods, are not equipped to handle the dynamic nature of 5G networks, leading to high false positives and low detection accuracy. In contrast, RL offers significant improvements in adaptability, detection accuracy, and response time. Materials and Method: The study simulates 5G network traffic and develops an RL-based IDS using Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) techniques. The performance of the RL-based system is compared to traditional IDS systems, focusing on detection accuracy, false positive rates, and response times. Results and Discussion: The RL-driven IDS demonstrated superior performance, achieving higher detection accuracy (95%) and faster response times (30 milliseconds) compared to traditional methods. However, challenges such as computational cost and model interpretability were identified. The study emphasizes the importance of adaptive learning mechanisms and the integration of RL into Zero Trust Architecture (ZTA) to enhance the security of 5G networks.