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The purpose of this study was to analyze the influence of proactive personality, OCB on teacher performance at Montessori Kaloran High School Using census techniques, the number of samples used was equal to the total population of 35 respondents. By using Smart PLS to analyze data is outer loading, inner model and hypothesis testing Then the results showed that 1) proactive personality has a significant positive effect on OCB, 2) OCB has no significant effect on performance,3) Proactive personality has a significant positive effect on performance, and 4) OCB is not a mediation between proactive personality and teacher performance.

The aim of this research is to empirically evaluate GCG and green accounting on CSR disclosure mediated by financial performance. The data used in this research is secondary data originating from the annual reports of mining companies listed on the Indonesia Stock Exchange (BEI) for 2012-2022. The number of samples collected using purposive sampling technique was 4 companies. The data analysis technique in the research uses the SEM-PLS program which is processed with WarpPLS 7.0. The research results show that Good Corporate Governance (GCG), which is proxied using the independent board of commissioners variable, has a negative effect on CSR disclosure, , green accounting has a positive effect on CSR disclosure, financial performance cannot mediate the influence of the independent board of commissioners on disclosure. CSR, financial performance cannot mediate the influence of the audit committee on CSR disclosure, financial performance cannot mediate the influence of green accounting on CSR disclosure.    

This research aims to determine and analyze the influence of the physical work environment, job training, compensation and work discipline on the performance of CV employees. Bintang Rema Utama Purwokerto production department. The population in this study were all employees of CV. Bintang Rema Utama Purwokerto production section in November 2023 as many 149 people. The samples obtained were 109 using the probability sampling method with a sampling technique, namely simple random sampling technique. This research uses multiple linear regression analysis tools with the help of SPSS statistics 26 software. The results of this research show that the physical work environment, job training, compensation and work discipline have a positive and significant effect on the performance of CV employees. Bintang Rema Utama Purwokerto production department. The implication in this research is that there is more attention from CV leaders. Bintang Rema Utama Purwokerto strives to improve the performance of its employees, such as providing lighting, circulation and room humidity that supports it, providing training materials that are easy to understand, providing adequate benefits and facilities, and warning employees whose attendance is not on time.

This research aims to determine and analyze the influence of job satisfaction, work discipline, work motivation, and work environment on employee performance at PT. Buana Spring Foam Purwokerto. This research is included in the quantitative research category. The population of this study consisted of 90 employees and 78 respondents were selected using probability sampling techniques with the proportionate stratified random sampling method.Research findings show that the performance of employees of PT. Buana Spring Foam Purwokerto is significantly and partially influenced by the following factors: job satisfaction, work discipline, work motivation, and work environment. The implications of this research suggest that companies must continue to pay attention to the promotion system, discipline, provide motivation, rewards or appreciation, and create a comfortable work environment and good relations between employees to improve their performance.

This research is based on the gap phenomenon in the form of the level of work achievement of employees which is considered not optimal. Condition can demonstrated through reports on the work achievements of Purbalingga Regency Education and Culture Service employees and research gaps in the form of differences in research results for each variable. The aim of this research is to analyze the influence of work motivation, training, competence and job satisfaction on the work performance of employees of the Purbalingga Regency Education and Culture Service. The population in this study ASN employees of the Purbalingga Regency Education and Culture Service, totaling 86 people with a sample of 75 respondents. The data analysis method used is multiple linear regression and hypothesis testing using t test calculations. The results of this research show work motivation, training, competence and job satisfaction influence work performance. From the results the research, can be implied that the leadership of the Purbalingga Regency Education and Culture Office needs to provide recognition to employees, needs to pay more attention to the qualifications of trainers for employees, increases responsiveness in carrying out work, needs to pay attention to the amount of payment an employee receives as needed.

This study explores how physical activity impacts mental health outcomes in college students. By examining data on exercise frequency, stress levels, and academic performance, the research identifies a positive correlation between regular physical activity and improved mental health, including reductions in stress and anxiety. The findings suggest that promoting physical activity could enhance mental well-being among students and improve academic performance.

This research aims to analyze the comparative financial performance of companies on the Indonesia Stock Exchange in the 2020-2021 period. The population in this study was 750 companies listed on the Indonesian Stock Exchange. The sampling technique in this research used stratified random sampling with 260 companies as sample. The data used in this research was secondary data obtained from financial reports and www.idx.co.id. The results of this research show that in general the sector that has the best financial performance in the period 2020 to 2021 is the healthcare sector, this can be seen from the company's financial ratios which are assessed from the current ratio, debt ratio and return on assets owned by the company in range of research years.

Background: Additive manufacturing (AM) requires reliable and efficient defect detection mechanisms to ensure structural integrity and product quality, yet conventional inspection approaches remain time-consuming and often unsuitable for real-time industrial deployment. Objective: This study aims to develop and experimentally validate an artificial intelligence based vision inspection system capable of accurately detecting surface defects in AM components. Methods: A Convolutional Neural Network (CNN) architecture utilizing pretrained backbones (ResNet and EfficientNet) was implemented with a transfer learning strategy and data augmentation techniques. High-resolution AM surface images representing porosity, cracks, and layer misalignment were used for training and evaluation. Model performance was assessed using Accuracy, Precision, Recall, F1-score, and mean Average Precision (mAP), and comparative benchmarking was conducted against traditional machine learning models such as Support Vector Machine and Random Forest. Results: The proposed CNN-based models significantly outperformed conventional approaches, achieving up to 95.1% Accuracy and 92.8% mAP. The EfficientNet backbone demonstrated superior generalization capability, particularly in balancing Precision and Recall, indicating robust defect detection performance across multiple categories. These findings confirm that AI-driven inspection frameworks provide scalable and reliable quality assurance solutions for advanced manufacturing environments.

Background: The rapid growth of warehouse automation and autonomous mobile robots has increased the need for adaptive navigation systems capable of operating safely and efficiently in dynamic industrial environments. Classical path planning algorithms such as A* and RRT perform well in structured settings but exhibit limitations when handling moving obstacles and environmental uncertainty. Objective: This study aims to develop and evaluate a reinforcement learning based navigation framework integrated with sensor fusion to improve path efficiency, collision avoidance, and robustness in dynamic warehouse scenarios. Method: An experimental research design was implemented combining high-fidelity simulation and real-world warehouse prototype testing. Deep Q-Network and Proximal Policy Optimization models were developed and trained using multi-sensor inputs from LiDAR, camera, and inertial measurement units. Performance was evaluated using path efficiency, collision rate, computational cost, and robustness metrics, with benchmarking against classical algorithms. Results: The results demonstrate that the Proximal Policy Optimization model achieved the highest path efficiency and lowest collision rate while maintaining stable computational performance under dynamic conditions. Reinforcement learning models significantly outperformed classical planners in adaptability and robustness, confirming their suitability for scalable industrial warehouse automation.

Background: The rapid advancement of Industry 4.0 has accelerated the integration of digital technologies such as the Industrial Internet of Things (IIoT), edge computing, and Digital Twin systems in smart manufacturing environments. However, many existing implementations remain fragmented and heavily dependent on centralized cloud infrastructures, resulting in latency constraints, limited scalability, and suboptimal real-time decision making. Objective: This study aims to develop and validate an integrated edge based Digital Twin optimization framework that combines IIoT sensing, hybrid edge cloud architecture, and reinforcement learning based adaptive control. Methods: The research adopts a multi phase design consisting of framework development, simulation based validation, and industrial pilot implementation. The proposed system integrates real time data acquisition, localized edge processing, Digital Twin synchronization, and intelligent optimization mechanisms to enhance operational efficiency. Results: The findings demonstrate significant performance improvements compared to conventional cloud based systems, including substantial latency reduction, increased production throughput, reduced downtime, and improved energy efficiency. Scalability and robustness testing further confirm that distributed edge intelligence enhances system resilience under increased workloads and network disruptions. These results indicate that integrating edge computing with Digital Twin modeling and reinforcement learning provides a scalable, responsive, and energy efficient solution for next-generation smart factories.

The rapid advancement of quantum computing has significantly impacted data security, as classical cryptographic algorithms such as RSA and ECC are increasingly vulnerable to quantum attacks. This study aims to evaluate the performance of classical and post-quantum cryptographic algorithms in a quantum simulation environment, focusing on stability, efficiency, and computational time. The research method employed experimental simulations using Qiskit, where cryptographic algorithms were modeled into quantum circuits and tested across varying qubit sizes of 128, 256, 512, and 1024. The simulation results indicate that classical algorithms face substantial limitations, with exponentially increasing computational time and drastically reduced stability beyond 512 qubits. In contrast, post-quantum algorithms demonstrated superior performance, maintaining high stability up to 1024 qubits, achieving greater quantum efficiency, and showing resilience against quantum attacks such as Shor’s and Grover’s algorithms. These findings highlight the urgent need to transition toward post-quantum cryptography as a more adaptive and reliable approach to safeguarding data in the quantum era. Although post-quantum algorithms still face certain challenges, such as larger key sizes and slightly higher computational costs at smaller scales, their overall benefits are far more significant in ensuring sustainable information security. Therefore, adopting post-quantum cryptography represents a strategic step that must be prioritized to address the evolving risks posed by quantum computing technologies.

The entrepreneurial world has entered the digital era, prompting MSMEs to embrace the digital realm. With 37 million female entrepreneurs in Indonesia, most of whom operate micro-businesses, there is a pressing need for special attention to be given to digital literacy and skills. This is crucial for enhancing business performance by leveraging their potential through digital skills and knowledge. This has been evidenced by research involving 72 female entrepreneurs who own MSMEs in Purbalingga Regency. The study utilized a quantitative approach with Regression Analysis tested using SPSS 25.0. The results indicate that digital skills and literacy significantly influence the performance of MSMEs, highlighting the importance of efforts to enhance digitalization skills and knowledge among female entrepreneurs in Purbalingga.

Financial performance analysis is analysis that used to measure company achievement from financial segment. this study purposes is to asses the impact of financial ratio, firm size and net profit margin to financial performance in banking sub-sector companies listed on Indonesia Stock Exchange Period 2020 - 2022. This research utilizes secondary data obtained from a sample of 45 companies. The analytical methodology employed in this study is descriptive analysis, normality test, hetroscedasticity, autocorelation, multicollinearity, multiple linear regression, coefficient of determination and F test from SPSS statistics 25 as the analytical instrument. The result found in this study show that Net Profit Margin variable has an influence on Return on Assets, meanwhile the Debt to Equity Ratio dan Firm Size variable does not exert influence Return on Assets

This research investigates the pivotal role of Emotional Intelligence (EI) in mitigating employee turnover from a psychological perspective. Emotional Intelligence—encompassing self-awareness, empathy, and stress management—is identified as a crucial factor in enhancing job satisfaction and organizational commitment, thus influencing employee retention. Employing a library research methodology, the study reviews existing literature on EI's impact on reducing turnover and highlights how high EI levels contribute to lower turnover rates by improving job satisfaction and fostering a stronger commitment to the organization. The study finds that employees with elevated EI are adept at managing workplace stress, fostering positive relationships, and aligning with organizational values. This alignment enhances their job satisfaction and commitment, leading to reduced turnover rates. The findings have practical implications for organizations, suggesting that integrating EI assessments into recruitment processes, developing EI training programs, and creating supportive work environments can significantly improve employee retention. Prioritizing EI development helps in building a more resilient and engaged workforce, thereby contributing to organizational stability and performance.

Solar energy is one of the most promising renewable energy sources to support the transition toward clean energy, yet conventional solar cell technology still faces efficiency limitations. Perovskite materials offer a potential solution due to their superior optoelectronic properties, although challenges related to stability and toxicity remain. This study aims to analyze the interaction of electromagnetic waves with perovskite materials and to evaluate the effect of nano-scale anti-reflective coatings on improving energy conversion efficiency. The research employed a combination of electromagnetic simulations using specialized software and laboratory experiments with miniature perovskite solar cell prototypes. Simulation results demonstrated higher electromagnetic field intensity within the active layer after the addition of anti-reflective coatings, with photon absorption increased by 15–18%. Experimental validation revealed that the energy conversion efficiency improved from 16.8–17.5% without anti-reflective layers to 20.5–21.3% with TiO₂- and SiO₂-based coatings. Optical characterization using a spectrophotometer confirmed enhanced light absorption up to 90% at specific wavelengths, while SEM analysis showed that smoother and more uniform SiO₂ coatings contributed to superior performance compared to TiO₂. Minor discrepancies between simulation and experimental results were attributed to fabrication variations, yet both approaches exhibited consistent improvement trends. These findings highlight that integrating perovskite materials with nano-scale anti-reflective coatings is a key strategy for enhancing the efficiency of next-generation solar cells while supporting future clean energy sustainability.

This paper discusses the integration of realworld applications into mathematics education as a means to increase student interest and comprehension. It outlines various teaching strategies used to connect mathematical theories with practical problems from fields such as engineering, economics, and everyday life. The study demonstrates that students who engage with realworld applications are better able to see the relevance of mathematics in their lives and are more motivated to learn. Additionally, the research evaluates the outcomes of these integrative approaches in terms of academic performance and student attitudes.

Company value is an important factor in determining the working conditions of an organization. Reflected through share prices as a form of company performance, company value is defined as a reflection of the appropriate and continuous policy setting by management so as to build market confidence in the company. The aim of the research was to understand and analyze the influence of carbon emission disclosure and green innovation on company value. The population in this research is all Sri-kehati indexed companies. Through a purposive sampling technique, the number of observations obtained in the 2020-2024 period was 84 companies. This research is included in the descriptive quantitative type category using secondary data. Annual report data is taken from www.idx.com or the company website. The technique for analyzing hypotheses applies multiple regression analysis. Research has proven that carbon emission disclosure has a positive effect on company value, and green innovation has a significant positive effect on company value. Simultaneously, disclosure of carbon emissions and green innovation influence company value..

This research aims to explain the influence of reward variables and partial motivation on employee performance. This research uses a quantitative method with the sample determination method used is Purposive Sampling with a sample size of 20 and collecting samples by distributing questionnaires. The data analysis used is descriptive statistical analysis. The results show the results of Validity analysis testing to determine the significance of the influence of rewards on work motivation. The table of questions regarding the influence of rewards on work motivation 1-6 above shows correlation values ​​of 0.738, 0.731, 0.665, 0.853, 0.678, 0.693, and the significant values ​​for the questions on the influence of rewards on work motivation 1-6 are 0.00 and 0.01, so it can be stated valid, and the results of the reliability statistics on the reward variable questions, the Cronbach's Alpha value is 0.654, this value exceeds the minimum value of 0.60, thus the questions in the reward variable table are declared reliable or reliable.

This study investigates the use of formative assessments in improving student learning outcomes in mathematics. The research focuses on how regular, low-stakes assessments help teachers identify student misconceptions, provide timely feedback, and tailor instruction to meet students' needs. The study finds that formative assessments lead to higher student performance, particularly in understanding complex mathematical concepts. The paper explores various formative assessment techniques, such as quizzes, peer reviews, and self-assessments, and their effectiveness in fostering student learning and achievement in mathematics.

This research investigates the role of digital tools in the teaching of science, comparing traditional instructional methods with those enhanced by technology. A series of classroom interventions were implemented in secondary schools, utilizing digital simulations and interactive platforms. The results indicate that technology-enhanced methods improved student engagement, understanding of complex scientific concepts, and academic performance. The paper highlights the potential of digital tools to transform science education and recommends strategies for their effective integration in the classroom.