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The purpose of this study is to find out the most important effect of transformational leadership on teacher professionalism. Development at SMP Swasta Muhammadiyah 01 Medan. This study method is a quantitative study survey research type. 60 teachers participated in this study. Principals and teachers were used as examples. The research instrument was a questionnaire. Dat collection instruments with questionnaires and data analysis techniques with normality test, linearity test and homogeneity test and hypothesis testing (using t-test). The result of this study show that there is a positive and significant effect between the main variable and teacher professionalism ini Muhammadiyah 01 Medan Private High School.  This is eviident form the result of the hypothesis testing, which shows that the main transformational leadership contributes 0.388 X 100% = 38.8% to teacher professionalism. The t-test performed gave a tnumber = 3.870 whilw the ttabel  value = 2.042. Since tnumber (3.870) > ttabel (2.042), it showsjthat there is a positive and significant effect in the form of a linear and predictive relationship between the main variable and professionalism of teachers in SMP Swasta Muhammadiyah 01 Medan. Regression line Ŷ = 46.654 + 0.322 X.

Diabetes Mellitus (DM) is a metabolic disorder characterized by high blood sugar levels due to insulin deficiency. Factors causing Diabetes Mellitus (DM) are lifestyle which includes diet, lack of exercise, monitoring blood sugar, and medication. Most people do not realize that they have DM and only find out when they experience severe symptoms. To avoid this, the k-Nearest Neighbor (KNN) method can be used to predict the possibility of developing diabetes. The aim of this research is to classify diabetes mellitus using the K-Nearest Neighbor (KNN) method and make people more aware of the risk of disease through healthy lifestyle changes. Data received from the Dharma Husada Clinic is categorized based on researchers' needs, including age, BMI, insulin, skin thickness, glucose, diabetes, genetics, and insulin. This research was carried out in three main steps: dataset input, preprocessing, and evaluation. The first stage is data analysis which begins by entering a dataset to train and test the model, where each data element has certain characteristics (attributes) and classes. Preprocessing steps include training data generation and data cleaning, which includes sanitization, lowercase, normalization, stopwords, stemming, and tokenizing. The final step is evaluating. Evaluation includes building an evaluation model and measuring the level of accuracy, building a predictive model, and saving the model. This research shows that the K-Nearest Neighbor (KNN) method can be used to classify diabetes mellitus (DM), but especially in a small dataset consisting of 245 dates and 8 attributes it is not accurate for patients aged 30 years. . A k value that is too small can cause overfitting, and a k value that is too large can cause underfitting. However, if the amount of data is small, the choice of k can have a large impact.    

The rapid growth of healthcare services has significantly increased the generation of medical waste, posing serious risks to environmental and public health, particularly through water quality contamination. This study examines global trends in medical waste management and proposes a hybrid analytical framework combining bibliometric analysis using SCIMAT and predictive modeling based on artificial neural networks implemented in Orange. Bibliometric mapping was employed to identify dominant research themes, temporal evolution, and knowledge gaps in medical waste and water contamination studies from 2000 to 2023. Subsequently, a neural network model was developed to predict potential water quality deterioration associated with mismanaged medical waste, using simulated environmental indicators. The results reveal a strong research focus on incineration, infection control, and hazardous waste, while predictive modeling of water contamination remains underexplored. The proposed hybrid approach demonstrates high predictive accuracy and offers a robust decision-support tool for environmental health policy. This study contributes methodologically and substantively to sustainable medical waste management and water resource protection.

Big data has revolutionized the landscape of natural sciences by providing extensive datasets that enable deeper insights and more accurate predictions. However, effectively analyzing such vast and complex data requires optimized machine learning algorithms tailored to specific applications. This study focuses on enhancing the performance of machine learning models in big data analysis for applications in natural sciences. The research aims to identify key optimization techniques, including feature selection, hyperparameter tuning, and algorithm customization, to improve model accuracy and computational efficiency. A combination of supervised and unsupervised learning approaches was applied to real-world datasets in fields such as climate science, genomics, and ecology. The findings demonstrate significant improvements in predictive accuracy and processing speed, highlighting the potential of optimized machine learning techniques in solving complex problems in natural sciences. The implications of this research extend to more efficient resource utilization and improved decision-making in scientific exploration and environmental management.

The development of digital technology, particularly Artificial Intelligence (AI), has brought significant changes to various industrial sectors, including accounting. In recent years, AI has become an essential tool used in various business applications, such as data processing, predictive analysis, and automated financial reporting. This technology can automate routine tasks like data entry and account reconciliation, which previously required human intervention, thereby increasing efficiency and reducing potential human errors. However, the application of AI in accounting has sparked discussions about the future of this profession. While AI can automate many aspects of accounting, it cannot fully replace human accountants, especially in areas requiring data interpretation, strategic decision-making, and ethical considerations. The webinar conducted for this study explored the implications of AI in accounting, highlighting both the opportunities and challenges of integrating AI into accounting practices. The findings emphasize the importance of adaptive accounting education and the need for continuous professional development to prepare future accountants to work in an increasingly digital and automated environment. Therefore, the future of the accounting profession will be determined by how AI and human accountants can work together to achieve the best outcomes, maintaining high ethical and professional standards.

The phenomenon of globalization has transformed the world's landscape, connecting nations, and inspiring the concept of Special Economic Zones (SEZs). The utilization of Foreign National Health Workers (FHNWs) has become a strategy to address the shortage of medical personnel and enhance healthcare services, particularly in remote regions. However, the implementation of this policy also faces several ethical, cultural, and linguistic challenges. The ethical aspect takes center stage in the utilization of FHNWs. Patient rights, professionalism, and cross-cultural collaboration are crucial focal points in healthcare service practices. Despite its benefits, the utilization of FHNWs encounters communication hurdles, especially when the employed language is unfamiliar. Additionally, the integration of local cultural values becomes a pivotal aspect in delivering sensitive and patient-centered care. This paper analyzes policy documents related to the utilization of Foreign National Health Workers (FHNWs) in Indonesia through normative and predictive approaches. The objective is to comprehend the concepts and regulatory implementations concerning the ethics of FHNW utilization within the context of Indonesian healthcare services. The phenomena of globalization and efforts to expedite economic growth through Special Economic Zones (SEZs) have influenced these policies. The analysis involves various policy documents relevant to FHNW utilization. In facing the ethical dilemmas arising in the practice of healthcare services by foreign medical personnel, it is imperative for them to adapt to the local culture without compromising the principles of medical ethics. Moreover, competition within the job market and cultural disparities between foreign and local medical personnel also present challenges that need to be addressed. In dealing with competition, a holistic approach involving the government, relevant institutions, and professional training for local medical personnel is required. Overall, the utilization of FHNWs holds potential benefits in addressing medical workforce shortages and enhancing healthcare access. However, this utilization must be carried out with ethical responsibility, respecting patient rights, and considering cultural and professional aspects. With a prudent approach, the utilization of FHNWs can contribute to improving the quality of healthcare services and the well-being of local medical personnel, in line with ethical principles and national interests.    

The main objective of the company is to maximise the value of the company. But a company can fail to increase company value, if it is not careful in applying the factors that affect company value. The purpose of this study was to determine the effect of dividend policy, institutional ownership, managerial ownership and audit committee on firm value proxied by TobinQ. The sample of this study is state-owned companies listed on the IDX for the period 2015-2021 with a total of 24 companies using purposive sampling method. Using the classic assumption test analysis method and hypothesis testing and multiple analysis, the analytical tool used is multiple linear regression panel data with the best model, namely the fixed effect model. The results showed that the t test for dividend policy was 2.375568 and the t table was 1.697 where t count> t table and a significant probability number of 0.025 <0.05 which means that dividend policy has a positive and significant effect on firm value. While institutional ownership, managerial ownership and audit committee have no significant effect on firm value. The F test results explain that the selected model is suitable for use in research. Then the results of the regression coefficient test (R²) indicate that the predictive ability of the 4 independent variables is 82.98%, while the remaining 17.02% is influenced by other factors outside the model which is not significant.

The presence of artificial intelligence (AI) technology has revolutionized efforts to protect data in offices. The challenges organizations face in maintaining information security are becoming increasingly complex as technology advances, but the benefits of AI provide an effective solution to optimize information security. In this summary, we discuss the challenges and benefits of AI in office data protection, focusing on enhancing information security.The first challenge is the increasing complexity of cyber attacks. Attackers are constantly looking for new vulnerabilities and using more sophisticated attack techniques to breach security systems. Adequate data protection is needed to address this challenge and prevent unauthorized access to critical company information. Additionally, a lack of knowledge about managing and monitoring security systems is a major challenge for many businesses.However, the use of AI to protect office data offers several advantages. First, AI's ability to quickly and accurately detect security threats aids in early detection of cyber attacks. Predictive analysis supported by AI can also detect dangerous patterns and prevent attacks before they occur. Furthermore, using AI to automate security processes can optimize operational efficiency and accountability for events that occur. AI-based security can significantly reduce the risk of data breaches and cyber attacks.The use of AI technology in office data protection is not only a supportive tool but also an innovative and efficient solution to address increasingly complex challenges in information security. We hope this overview provides a deeper understanding of the challenges and benefits of AI in protecting office data, as well as efforts to optimize information security in this digital era.  

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.

The rapid advancement of deep learning technologies has significantly transformed healthcare analytics, particularly in medical data prediction and classification. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework for multi-modal healthcare data analysis, integrating medical imaging, structured electronic health records (EHRs), and IoT-generated time-series physiological signals. The proposed architecture combines spatial feature extraction through CNN with temporal dependency modeling via LSTM to enhance predictive accuracy and clinical decision support. A quantitative experimental design was employed, utilizing multi-source healthcare datasets that underwent preprocessing, normalization, and feature engineering prior to model training. The performance of the hybrid model was evaluated using Accuracy, Precision, Recall, F1-Score, AUC-ROC, and Mean Absolute Error (MAE), and compared with conventional machine learning models and standalone deep learning architectures. Experimental results demonstrate that the proposed CNN–LSTM model achieves superior performance, with improved classification accuracy and reduced prediction error, while maintaining strong generalization capability. The findings indicate that integrating spatial and temporal feature learning significantly enhances disease detection, risk stratification, and personalized treatment planning. This approach supports the development of intelligent clinical decision support systems and scalable smart healthcare environments. The proposed framework offers a reliable and efficient solution for advanced healthcare analytics in IoT-enabled systems.

The rapid development of smart city initiatives has significantly increased the adoption of Internet of Things (IoT) technologies to enhance urban services, infrastructure efficiency, and quality of life. However, the large-scale deployment of interconnected IoT devices also introduces critical cybersecurity challenges, including unauthorized access, data breaches, and system vulnerabilities. This study aims to develop an integrated IoT security management model to improve cybersecurity resilience in smart city environments. The research adopts a Design Science Research (DSR) approach, which involves problem identification, literature analysis, model design, implementation, and evaluation. The proposed model incorporates key security components such as Identity and Access Management (IAM), device authentication, secure communication through encryption, firmware and patch management, and continuous monitoring with intrusion detection mechanisms. The model is evaluated through simulation in smart city scenarios, including transportation systems, environmental monitoring, and energy management. The results demonstrate significant improvements in security performance, with increases in threat detection rate, vulnerability reduction, access control effectiveness, and system stability under attack conditions. Quantitative analysis shows improvements of up to 37% compared to conventional approaches, indicating the effectiveness of the proposed model in mitigating IoT-related cybersecurity risks. This study contributes by providing a comprehensive and scalable framework for IoT device security management, which can be applied to enhance the reliability and sustainability of smart city systems. Future research is recommended to validate the model in real-world implementations and integrate advanced technologies such as artificial intelligence for predictive threat detection.

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.

Power systems are critical infrastructure that face significant challenges due to increasing demand and inherent complexity. Predicting failures in power systems is crucial for enhancing grid reliability, minimizing downtime, and optimizing maintenance processes. This study evaluates various deep learning models, specifically convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models, for predicting power system failures. By analyzing these models’ performance metrics on historical power grid data, the study provides insights into the strengths and weaknesses of each approach. The findings contribute to the development of more robust predictive models for power system reliability.

This paper compares various machine learning models in their ability to predict financial trends, with a focus on time-series analysis. We evaluate models such as linear regression, decision trees, support vector machines, and deep learning, measuring their performance based on accuracy, computational cost, and interpretability. Our results reveal that deep learning models offer superior accuracy but are less interpretable, while simpler models, though less accurate, provide better insight into the underlying data. This research provides guidelines for selecting suitable models based on specific financial applications.

The research employs a quantitative approach using statistical methods, where the data consists of numerical values obtained through the use of questionnaires and surveys. The total population in the production department of PT SKF Indonesia is 255 employees, and the sampling is done using the Purposive Sampling technique with the Slovin formula to determine the sample. The sample used is 72 employees from the production department of PT SKF Indonesia. This study utilizes Smart PLS 3.0 software and conducts testing, including data analysis methods (descriptive and verificative), outer model testing for validity and reliability, inner model testing for R-Square (R2), Predictive relevance (Q2), and model fit testing (Goodness of Fit), as well as hypothesis testing including T-test (Partial) and F-test (Simultaneous). The results of this study, in partial, show that the workload variable has a P-Value of 0.001 or < 0.05, indicating that the workload has a positive influence on work enthusiasm. The working environment variable has a P-Value of 0.000 or < 0.05, signifying that the work environment has a positive influence on work enthusiasm. Lastly, the simultaneous results indicate that the workload and work environment variables have a positive and significant impact on work enthusiasm, with an F-value of 923.83 > F-Table 3.13 and a coefficient of determination (R2) of 96.4% in R-Square.

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.

PT The lathe is one of the essential machines in production activities in this company. The amount of downtime caused by machine failure will reduce product quality and quantity. Therefore, this research aims to identify the factors that most influence the risk of lathe failure so that repair priorities and maintenance strategies can be determined. This can be achieved by using the Failure Mode and Effect Analysis (FMEA) method. FMEA is a method that can be used to identify the causes and impacts of each possible failure mode on machine components by systematically explaining the levels of failure so that appropriate prevention or repair can be carried out. The FMEA method shows the Risk Priority Number (RPN) value as a reference in determining the choice of maintenance strategy, namely, predictive maintenance, preventive maintenance or corrective maintenance. The research results show that the highest RPN value is found in the motor shaft pulley component at 320 and Nut Screw at 210. Maintenance priorities are determined based on the Pareto diagram principle. The appropriate maintenance strategy carried out by PT.   Keywords: FMEA, Lathe, Maintenance, RPN

The frozen food business continues to experience an increase in transaction value from 2020 to 2022. However, it is different from the Omahnong, which has decreased from 2020 to 2022. In solving the problem, new business innovation is needed. The research aims to find out the business model canvas that will be applied after doing ten types of innovation, the ten types of innovation strategy that has been carried out and the new ten types of innovation strategy. This research uses qualitative research methods with a descriptive approach. The results of the study found nine elements of Omahnong's business model canvas after doing ten types of innovation. In addition, it was found that the ten types of innovation strategies that have been carried out by Omahnong are cost leadership, flexible manufacturing, superior product and community and belonging and the results of the new and appropriate ten types of innovation strategies for Omahnong are cost leadership, predictive analysis, superior product, loyalty program, and process automation. This research can be used as a reference for entrepreneurs in innovating business strategies and can help Omahnong in developing its business.

The role of fractal geometry in analyzing growth patterns of tropical plants and its application in precision agriculture has become an emerging interdisciplinary topic in the modern era. Tropical plants often exhibit complex and irregular structures that cannot be fully described by conventional Euclidean geometry. This study aims to examine fractal-based mathematical models to identify self-similar patterns in tropical leaves and to explore their potential for optimizing precision farming practices. The methodology employs image-based mathematical analysis, using digital images of tropical plants to measure fractal dimensions and quantify growth complexity. The findings reveal that consistent fractal patterns can be observed across different species of tropical plants, particularly in leaf venation and branching structures, indicating a universal growth principle. Such patterns demonstrate high predictive potential for estimating biomass, monitoring plant health, and assessing responses to environmental changes. Furthermore, the study highlights how fractal-based approaches, when combined with precision agriculture technologies, can improve resource efficiency by supporting accurate irrigation scheduling, soil quality monitoring, and yield forecasting. The implications extend to sustainable agricultural development, as fractal analysis provides a scientific foundation for balancing productivity with environmental preservation. In conclusion, this research underscores the significance of fractals not only as mathematical concepts but also as powerful analytical tools with practical benefits, offering new pathways to advance digital farming, ecological monitoring, and sustainable food security in the modern era.

Property price determination is a complex challenge influenced by various factors, thus requiring an effective method for accurate prediction to support investment decision-making. In the current digital era, conventional approaches are being replaced by data-driven and artificial intelligence methods, where Linear Regression remains a popular choice due to its simplicity and effectiveness in modeling linear relationships. This study aims to analyze the relationship between the physical characteristics of a house and its selling price, and to build an accurate predictive model using the Linear Regression algorithm. A quantitative method was used, focusing on Building Area , Number of Rooms, and Building Age  against the House Selling Price. Correlation analysis results show that Building Area has the strongest correlation (0.81) with price, while Building Age shows a negative correlation (-0.52). The Linear Regression model demonstrated very strong and stable performance. The model achieved an R² Score of 0.9396 on the testing data, meaning 93.96% of house price variability can be explained by the model. Furthermore, the low MAE of only 11.31 million rupiah indicates a small prediction error, and the consistency of R² scores confirms that the model does not suffer from overfitting. This study concludes that the Linear Regression model provides excellent, stable, and reliable prediction performance for projecting house selling prices