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One of the efforts to overcome teacher problems in learning is by educational supervision. Supervision is guidance in the form of guidance or guidance towards improving the educational situation in general and improving the quality of teaching and learning. Supervision is carried out by the Principal (supervisor) whose role is to supervise teaching and learning activities, teacher performance etc. The purpose of supervision is to develop better situations to train the learning process and improve the teaching profession. The study was conducted because researchers wanted to get a better understanding of a special case This research reveals specifically about the implementation of teaching supervision and teacher performance in the learning process of Islamic religious education in MTSN 2 Bengkalis. Based on the entire series of analysis and discussion of data, it can be concluded that the implementation of principal supervision of the performance of Islamic religious education teachers in MTSN 2 Bengkalis is The planning of principal supervision is carried out in a planned, systematic and continuous manner. At this stage the principal determines the name of the teacher to be observed, forms a determination of the time for the implementation of the observation time, compiles an observation grid and determines whether the class observation is known to the teacher or not, this is based on an agreement between the principal and the Islamic religious education teacher. In the implementation of supervision, the principal's attitude did not interfere with the process of learning Islamic religious education in the classroom even the principal at the end of class hours gave motivation to students to continue learning diligently. In the follow-up stage, the results of supervision are discussed jointly between Islamic religious education teachers and school principals who discuss teacher deficiencies in teaching in the hope that in the future teachers can improve them.

Effective school leadership is critical for enhancing teacher performance and professionalism. This research aims to analyze the application of the school principal's charismatic leadership style in improving teacher performance and professionalism at an elementary school in Indonesia. Employing a descriptive qualitative research design, this study systematically explores the unique traits and behaviors of charismatic leaders, including their strong charm, self-confidence, clear vision, and ability to inspire followers. While charismatic leadership positively impacts values, ideology, and participation, it can also lead to a focus on personal loyalty and the use of power for personal gain. The success of charismatic leadership is evidenced by the close relationship between leaders and followers, with followers showing high commitment to the leader's vision. This study contributes to the education field by providing insights into how charismatic leadership can be harnessed to improve teacher performance and professionalism, highlighting both its benefits and potential drawbacks.

JNE is the largest logistics company in Indonesia, this company is a freight forwarding company both domestically and abroad. In its business process JNE has an Outbound unit which is responsible for receiving goods from Sales Counter Official (SCO) agents and branch offices, processing, checking, packaging, and transporting goods to the appropriate destination. The purpose of this study is to determine the effect of Standard Operating Procedures (SOP), supervision, and Information Technology on Employee Performance in the operational management of the JNE Main Branch Medan Outbound unit. This research uses Descriptive Qualitative method in identifying problems. The results in this study state that SOP has a significant effect on Employee Performance because it is an important part of the smooth work process for employees, Supervision and Information Technology also have a significant effect on Employee Performance. Supervision ensures employees work in accordance with SOPs and achieve previously set work targets, and the use of Information Technology in the work process will increase the efficiency and effectiveness of employee work.

PT XXX is a garment company on an international scale. Technical staff plays an important role in maintaining operational performance, especially in facing challenges during rapid style changes and potential operational risks. This research aims to identify the causes of low productivity of technical staff performance, design improvement strategies with a focus on productivity improvement, and evaluate the effectiveness of the Failure Mode and Effect Analysis (FMEA) method in improving the technical performance of technical staff. The results of this research show that the FMEA method is effective in identifying potential failures, helps evaluate the level of risk by calculating the Risk Priority Number (RPN) value, and is a reliable tool for designing strategies to improve technical staff performance.

This study examines the impact of blended learning on student engagement and academic achievement in secondary schools during the recovery phase of the pandemic. A quantitative research approach was employed, utilizing a comparative design to assess the differences between blended learning and conventional learning models. Data were collected through student engagement surveys and academic performance records from secondary school students exposed to both learning models. The results indicated that students in the blended learning model exhibited significantly higher levels of engagement across behavioral, emotional, and cognitive dimensions compared to those in the conventional model. Academic achievement was also notably higher in the blended learning group. Statistical analysis, including t-tests and linear regression, revealed a strong positive correlation between student engagement and academic performance. The study concluded that blended learning significantly enhances student involvement and performance, particularly when supported by adequate technological infrastructure and teacher preparedness. These findings suggest that blended learning should be integrated as a permanent strategy in post-pandemic education to support student success and engagement. The study also emphasizes the importance of continuous teacher training and technological support in maximizing the benefits of blended learning.

This study explores the development of low-carbon concrete by integrating industrial waste materials and CO₂-absorbing minerals to reduce carbon emissions in the construction industry. The research investigates various mix ratios involving fly ash, slag, and CO₂-absorbing minerals, aiming to optimize both performance and sustainability. Experimental methods included compressive strength testing, carbon emission measurement, and durability evaluation. The results indicate that low-carbon concrete formulations achieve up to 45% reduction in carbon emissions compared to conventional concrete, which uses Portland cement. Despite a slight decrease in compressive strength, the optimal mix reached 34 MPa, which is sufficient for structural applications. Durability testing revealed that concrete with CO₂-absorbing minerals showed improved long-term performance compared to other formulations. The findings highlight the potential of using industrial waste materials and carbon capture technologies to create more environmentally sustainable concrete while maintaining necessary structural properties. This study contributes to the growing demand for eco-friendly construction materials and supports the implementation of low-carbon concrete in large-scale industrial applications. Further optimization of mix ratios and long-term performance studies are recommended for broader adoption in the construction industry.

This study explores the role of sentiment analysis as a predictive tool for understanding and forecasting product launch success in the digital market. Sentiment analysis involves the classification of consumer sentiment expressed on social media platforms such as Twitter and Instagram, and it can significantly impact businesses by predicting consumer behavior and product performance. The research highlights the relationship between social media sentiment and product success, demonstrating that positive sentiment is strongly correlated with higher sales and consumer engagement, while negative sentiment can lead to declines. Machine learning models, including Support Vector Machines (SVM) and Random Forest, were employed to classify sentiment from large volumes of social media data and correlate it with product performance indicators such as sales volume and consumer interaction. The study found that sentiment analysis models were highly effective in predicting product success, with positive sentiment generally driving product profitability and negative sentiment posing a potential threat to brand reputation. Moreover, the analysis showed that social media sentiment provides real-time insights into consumer perceptions, enabling businesses to quickly adjust marketing strategies and product development plans. These findings underscore the importance of integrating sentiment analysis into product launch evaluations and strategic decision-making. Future research should explore the integration of sentiment analysis with other predictive market models and investigate the effects of fake reviews and post-purchase consumer behaviors on product success.

This study investigates the impact of digital gamification on employee motivation, engagement, and productivity in technology companies. Gamification, which integrates game design elements such as rewards, badges, and leaderboards into non-game environments, has gained popularity as a tool for enhancing employee performance in workplaces. The research employs a quantitative survey methodology to gather data from employees in technology firms that have implemented gamified systems. The findings reveal that gamification significantly increases employee motivation by tapping into intrinsic motivators such as autonomy, competence, and relatedness. Employees reported higher levels of engagement, with many attributing their increased participation to the fun and competitive nature of gamified tasks. Additionally, productivity improvements were observed, particularly among employees in roles that require individual performance, such as software engineers, where autonomy and goal-setting were key factors. The study also identifies variations in the effectiveness of gamification based on factors such as job role and organizational culture, emphasizing the need for customization to suit different employee needs. Despite these positive outcomes, challenges such as employee resistance, poorly designed systems, and resource constraints were noted as barriers to successful gamification implementation. These findings suggest that gamification is an effective strategy for improving motivation and engagement in technology companies but requires thoughtful design and alignment with company culture to maximize its impact. Future research should explore the long-term effects of gamification and examine how specific elements, such as leaderboards and rewards, influence employee behavior across different demographics.

The Muara EnimWelcom Dance is a traditional dance in Muara Enim Regency, the welcome dance is still developing today. The aim of this research is to describe the form of presentation of the welcome dance. To strengthen the research, the research interviewed 3 informants Dian PermanaJoeliusS.Pd, Elani DestiaristaS.Pd, Sri WulandariS.Pd, people who had received information about the welcome dance, from its presentation to how the welcome dance works displayed. The benefits of this research are to preserve existing arts, and it is hoped that it will be useful for: increasing the insiht of researchers and readers of this article, to know what the Muara Enim welcome dance is, for similar researchers so that it can be used as a reference. This research uses qualitative methods, data collection and retrieval techningques using observation, interviews and documentation techniques. The results of this researchis that the welcome dance is a form of presentation consisting of dancer are walking on tiptoe, hands stacked, opening right and left, praying up, picking right and left up. The music used in the welcoming dance includes drums, kromongan, simbal and gong. The make-up used by dancers for female dancers uses beautiful make-up while male dancers use handsome make-up. The costumes for the welcoming dance are clothes brackets, songketclosth, shawls, paksangko crowns, potpourri of flowers. The floor design is in the shape of the letter V and has straight horizontal and vertical lines. The property used by dancers of tanggai, betel taps, umbrellas and spears. The performance venue is like a performance stage in general.      

This research aims to analyze the usage of the BRIMEN application and its impact on the ease of performance of Credit Administration Officers (ADK) at the Pasuruan Branch Office of PT Bank Rakyat Indonesia (Persero) Tbk. The research method used is qualitative method with a participatory approach and descriptive research type. Data were obtained through observation, interviews, and direct documentation in the field. The results show that the BRIMEN application facilitates ADK officers in various tasks such as scanning customer documents, uploading files to the system, and archiving physical files. Although still in the refinement stage, BRIMEN provides time efficiency and flexibility for officers, who previously performed processes manually. In conclusion, BRIMEN has provided benefits in improving the work efficiency of Credit Administration Officers (ADK) at the Pasuruan Branch Office. Although further refinement is needed, this application has the potential to be continuously developed to enhance productivity and operational efficiency.

The rapid growth of cross-platform applications has significantly increased the volume and diversity of sensitive user data processed across heterogeneous and distributed environments. Personally identifiable information, device identifiers, behavioral data, and financial information are routinely collected to support personalization, analytics, and service optimization. While these practices enhance application functionality and user experience, they also introduce substantial privacy risks, including unauthorized data access, device fingerprint–based re-identification, cross-user data leakage, and large-scale data breaches. These risks are further amplified by distributed processing architectures and extensive third-party library integrations commonly used in modern cross-platform systems. This study aims to systematically analyze privacy issues in cross-platform applications by examining the types of sensitive data involved, identifying dominant privacy threats, and reviewing state-of-the-art privacy-preserving mitigation strategies. A systematic literature-based methodology was employed, focusing on recent Scopus-indexed journal articles, conference papers, and book chapters. The analysis synthesizes findings using thematic categorization and a conceptual research framework that maps sensitive data sources to privacy threats and corresponding mitigation mechanisms. The results indicate that privacy risks in cross-platform applications originate not only from external attacks but also from internal architectural weaknesses, such as flawed authorization logic and excessive data sharing across system components. Privacy-preserving techniques including differential privacy, federated learning, blockchain-based data governance, secure multi-party computation, and fine-grained access control mechanisms are shown to provide stronger privacy guarantees compared to conventional centralized approaches. However, these techniques also present trade-offs related to system complexity and performance. Overall, the study highlights the importance of adopting a multi-layered, privacy-by-design approach to ensure sustainable, trustworthy, and regulation-compliant cross-platform application development.

The rapid growth of Internet of Things (IoT) and edge computing technologies has introduced new security challenges due to the distributed, heterogeneous, and dynamic nature of these environments. Conventional static security mechanisms, such as rulebased authentication and fixed trust models, are often inadequate for addressing evolving threats and abnormal behaviors in largescale IoT systems. To overcome these limitations, this study proposes a machine learningbased trust evaluation framework for enhancing security in distributed IoT environments. The proposed approach dynamically assesses the trustworthiness of IoT nodes by analyzing behavioral and interactionbased features collected at the edge layer. Machine learning models are trained to classify nodes into trusted and malicious categories and continuously update trust values in response to changing network conditions. Based on the predicted trust levels, adaptive security decisions are enforced to allow or restrict node participation in data sharing and computation processes. A quantitative experimental evaluation is conducted using simulated distributed IoT scenarios that include both normal and malicious behaviors. The performance of the proposed framework is evaluated using standard metrics such as accuracy, precision, recall, F1score, and detection effectiveness, and is compared against conventional static trust and rulebased security mechanisms. The results demonstrate that the proposed machine learningbased trust evaluation approach achieves significantly higher detection accuracy and robustness while maintaining low computational overhead. Overall, the findings confirm that integrating machine learning into trust management provides an effective and scalable solution for securing distributed IoT systems under dynamic and adversarial conditions.

This research explores the potential of local bio-composites made from bamboo and coconut fiber as sustainable building materials in Indonesia. The construction sector in Indonesia faces significant environmental challenges, particularly the high carbon emissions associated with conventional materials like concrete and steel. The primary objective of this research is to evaluate the environmental and economic benefits of substituting these traditional materials with bio-composites, using Life Cycle Assessment (LCA) and energy optimization simulations. The LCA methodology evaluates the environmental impact of bio-composites across their entire life cycle, comparing them with conventional materials in terms of carbon emissions, energy consumption, and waste. Energy optimization simulations focus on assessing the thermal performance and overall building efficiency when using bio-composites in construction. The main findings suggest that bio-composites exhibit a significantly lower carbon footprint and better thermal insulation properties than concrete and steel, contributing to reduced energy consumption in buildings. Additionally, the use of locally sourced materials like bamboo and coconut fiber offers economic advantages, such as lower transportation costs and support for local economies. The research concludes that bio-composites can serve as viable alternatives to traditional materials, providing both environmental and economic benefits. For successful adoption, the research recommends policy support, technological advancements, and educational initiatives to promote the use of bio-composites in Indonesia’s construction industry.

The increasing complexity and heterogeneity of data in Smart Information Systems pose significant challenges for effective decision-making. While data fusion techniques have been widely adopted to integrate multiple data sources, traditional fusion approaches often fail to consider contextual information, resulting in limited interpretability and reduced decision relevance. This study proposes a contextual data fusion approach that integrates heterogeneous data sources with contextual attributes, including temporal, spatial, and operational context, to enhance decision accuracy and robustness. The research employs a computational and experimental methodology involving data preprocessing, context encoding, multi-level data fusion, and performance evaluation. Experimental results demonstrate that the proposed approach outperforms single-source analysis and non-contextual data fusion in terms of accuracy, precision, recall, and F1-score, with only a marginal increase in computational cost. The findings confirm that incorporating context into the data fusion process significantly improves the quality and reliability of analytical outcomes. This study contributes to the development of intelligent and data-driven systems by highlighting the critical role of contextual awareness in supporting transparent and effective decision-making in Smart Information Systems.

Artificial Intelligence (AI)-based Decision Support Systems (DSS) have become a central component of digital transformation initiatives across various industries. While prior studies have primarily emphasized technical aspects such as accuracy, performance, and computational efficiency, less attention has been given to the integration of human-centered principles and scalable architectural design. This study aims to examine how AI-based DSS can be enhanced through the combined application of Human-Centered Artificial Intelligence (HCAI) principles and scalable AI architecture. Using a qualitative, literature-based research methodology, this study systematically analyzes peer-reviewed publications indexed in Scopus to identify key dimensions influencing the effectiveness and sustainability of AI-driven DSS. The findings indicate that technical capabilities alone are insufficient to ensure successful adoption and long term impact. Instead, transparency, explainability, ethical governance, and user empowerment core elements of HCAI are critical for fostering trust and user acceptance. Furthermore, scalable architectural principles, including modularity, interoperability, and adaptability, are essential for enabling AI-based DSS to operate reliably in large-scale and dynamic environments. This study contributes a unified conceptual framework that bridges technical scalability and human-centered design, offering theoretical insights and practical guidance for developing trustworthy, scalable, and sustainable AI-based Decision Support Systems in digital transformation contexts.

This study explores the relationship between teachers' digital pedagogical competency and student learning outcomes in both rural and urban schools. A descriptive survey design, utilizing correlational and regression analyses, was employed to examine this relationship. Data were collected from teachers using a structured questionnaire assessing their digital competence, while student learning outcomes were measured through academic performance records. The results revealed significant differences between urban and rural schools in terms of teachers’ digital competence, with urban teachers demonstrating higher levels of digital skills. A positive correlation was found between teachers' digital competence and student learning outcomes, particularly in urban schools, where digital tools were more readily available. The regression analysis further confirmed that digital competence was a significant predictor of student achievement, with urban schools outperforming rural ones even after controlling for teacher experience. The study suggests that infrastructure availability, teacher training, and institutional support play crucial roles in enhancing digital competence, particularly in rural schools, where such resources are often limited. These findings underscore the need for policy reforms that ensure equitable access to digital tools and professional development opportunities for teachers across both rural and urban settings. Recommendations include targeted interventions for rural schools to bridge the digital divide and improve student learning outcomes through enhanced teacher digital competencies.

The work POLA-POLA was inspired by the online game Higgs Domino where the creator was interested in the algorithmic patterns of the game. This game is not only played by teenagers but is also played by children and even the elderly. This game has its own uniqueness, namely that there are boxes where the spin moves from top to bottom so that wins and losses appear in the box. The artist interpreted these boxes in the work POLA-POLA by using six cubes of different sizes and each side has a different color, consisting of red, yellow, green and white. In creating the dance work entitled POLA-OLA, the methods of observation, exploration, improvisation and formation were used. Supporting the work consists of six dancers, namely three male dancers and three female dancers. The POLA-POLA dance work was performed at the Hoerijah Adam Performance Building, Padangpanjang Indonesian Arts Institute. This dance work is presented in the form of a contemporary dance work.

The study conducted at the National Potential Young Athletes Training Center (SLOMPN) of Unesa provides a crucial perspective on the use of an Android-based match statistics application. The research aims to optimize athletes' potential through the Long Term Athlete Development (LTAD) approach, integrating knowledge of athlete development with the application of technology in training. The survey results from 5 coaches indicate that the application aids in planning training, understanding application features, and assessing athlete development with positively skewed average scores. However, there is room for improvement, especially in terms of comfort during matches and communication with athletes. Meanwhile, 18 athletes showed positive acceptance of the application by rating its ease of use and impact on performance enhancement highly. Nevertheless, there is a need for improvement in injury recovery features and data security. This study underscores the importance of continuous collaboration between application developers and users to enhance the effectiveness of technology in sports training.

Measuring the company level of productivity performance is a very important thing to do to see and evaluate achievements and improvements to productivity levels that are not yet optimal. This case study was taken from the leather bag UKM which is currently experiencing a decline in productivity during the 2020 period. With the increase in working hours, the company must increase workers' overtime costs. In this research, the method used to measure the level of company productivity is the objective matrix or OMAX method and this research refers to the OMAX steps, namely determining productivity criteria on the production line, determining weights, determining levels on the objective matrix, calculating the productivity index, and productivity analysis with a traffic light system. The research results show that the average value of overall productivity in 2020 is 4,388 The highest productivity index was achieved in August amounting to 67,01%. From the traffic light system, it is known that the worst indicator of productivity is the defect factor, so improvements need to be made regarding this.

This journal examines the Piriang Talam Bantiang dance attraction at Sanggar Talang Sarumpun as a form of performing art in the city of Payakumbuh. The method used is a descriptive-analytical qualitative research method, which involves presenting data obtained in the field and then analyzing it according to the research needs. This study employs the theory and opinions of Y. Sumadiyo Hadi regarding form to strengthen the writing. Additionally, it uses supporting theories from Dedi Supriadi on creativity. The results obtained from this research show that the Piriang Talam Bandantiang dance attraction is a form of performance that can attract the attention of the community to watch and enjoy, while simultaneously introducing Piriang Talam Bandantiang dance as a performing art in the region.