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This study is a field research that attempts to see the extent of the application of the Al-Miftah Lil Ulum method in learning yellow books at the Tarbiyatul Aulad Sukadana Islamic Boarding School, Kayong Utara Regency. The success or failure of learning can be seen from the accuracy in choosing the method. This study aims to describe how the planning, implementation and supporting and inhibiting factors in the application of the Al-Miftah Lil Ulum method in learning yellow books at the Tarbiyatul Aulad Sukadana Islamic Boarding School, Kayong Utara Regency. The data collection methods used in this study are; observation , interviews and documentation. Data analysis was carried out through four stages, namely data collection, data reduction, data presentation, and drawing conclusions. The results of the study indicate that 1) The planning of the Al-Miftah Lil Ulum method in learning yellow books at the Tarbiyatul Aulad Islamic Boarding School includes a) the ustadz first reads the learning objectives in the Al-Miftah Lil Ulum method guidebook b) the ustadz first muthala'ah again the material to be delivered, c) the ustadz tells the students to memorize vocabulary and nadzhom imrithi and alfiyah related to the material to be studied. 2) The implementation of the Al-Miftah Lil Ulum method in learning yellow books at the Tarbiyatul Aulad Islamic Boarding School includes the following steps : Opening with Prayer and Intention, Introduction to Vocabulary (Mufradat), Gradual Understanding of Nahwu and Shorof Rules, Application of Rules Through Practical Exercises, Memorizing Vocabulary and Rules, Dialogue and Light Conversation (Muhadatsah), Reading and Translating Texts, Periodic Evaluation and Tests, Reinforcement with Yellow Books (Advanced Level) . 3) supporting factors: availability of competent Ustadz, structured and gradual curriculum, student discipline in memorizing and muthala'ah, supportive learning environment, inhibiting factors: differences in academic background of students, limited learning time, lack of supporting learning media, physical fatigue of students.

This study aims to design an effective cash accounting information system for an agrotourism hotel in Batu City. The data collection methods used are field studies and literature review, employing techniques such as observation, interviews, and documentation. The important significance of this research is to enhance the cash receipt procedures for hotel room rental payments by incorporating additional digital payment methods through e-wallets and QRIS, thereby increasing transaction efficiency that benefits both the hotel and consumers. The significant importance of this research is to enhance the efficiency of payment transactions, making it easier for both the hotel and consumers. The qualitative analysis in this study aims to improve the accuracy of cash recording, reduce the risk of errors and misuse of funds, enhance internal control, broaden payment options, and provide practical guidance for hotels and consumers regarding payment and cash receipt procedures. With improved cash receipt procedures, the hotel will be better prepared to meet consumer needs and increase competitiveness in response to changes.

Customer satisfaction is a crucial indicator in assessing the quality of a company's products, services and overall experience. This research aims to identify the level of customer satisfaction and optimize the available data for effective use in sentiment analysis. In this study, we analyzed 4,353 customer reviews collected over the past year, with 3,481 reviews used as training data and 871 reviews as testing data. The analysis process was conducted using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and leveraged the Logistic Regression algorithm to build a predictive model. Model evaluation using the confusion matrix yielded an accuracy of 94.60%, a precision of 94.26%, and a recall of 94.60%. The analysis was conducted using Jupyter Notebook and the Python programming language. The results indicate that sentiment analysis is effective in identifying and predicting customer satisfaction levels, which in turn can help a company’s products improve its service strategies. The optimization of previously underutilized data now provides deeper insights into customer perceptions and expectations, enabling the company to make more targeted decisions and enhance overall customer satisfaction.

This article examines the role of language and communication in journalism, with a focus on conveying accurate information. In an era where information is disseminated rapidly and in a variety of ways, journalists need to not only report the news but also ensure that the news is credible. This article contributes to a deeper understanding of the relationship between language, communication, and news accuracy by analyzing effective communication methods and the challenges journalists face in maintaining accuracy. The aim is to: This study shows that the use of appropriate language and good communication techniques can increase the credibility of the media.

This study evaluates the effectiveness of a hybrid citizen–AI legal monitoring system in enhancing urban environmental governance. The hybrid system integrates citizen-driven reporting platforms with AI-powered legal monitoring tools to address the challenges of weak public oversight in urban environmental management. By implementing the system in three metropolitan areas, the study explores how real-time data collection through citizen reports, combined with AI-driven analysis, can improve the accuracy, speed, and responsiveness of identifying environmental violations. The results showed a 45% improvement in oversight effectiveness, demonstrating the potential of hybrid systems to enhance monitoring capabilities beyond traditional methods. The AI system, capable of analyzing large datasets and providing timely insights, enabled quicker identification and categorization of violations such as pollution and waste management issues. The integration of citizen involvement through digital platforms allowed for more inclusive data collection, enhancing the quality and volume of information available for decision-making. This synergy between human participation and AI-driven analysis improved the speed of response to urban environmental challenges, making the system more adaptive and efficient. However, challenges such as data reliability and variable citizen participation rates were identified, suggesting the need for strategies to encourage consistent engagement and ensure the accuracy of reported data. The study concludes that hybrid citizen–AI systems can significantly improve urban governance by enhancing transparency, accountability, and responsiveness, offering a promising solution for cities seeking to address environmental issues more effectively.

This study explores the application of machine learning to predict students' GPA based on behavioral and time-related factors, including study hours, extracurricular activities, sleep, social interactions, and physical activity. Seven regression algorithms were employed to evaluate predictive accuracy using metrics such as MAE, RMSE, and R2 Among these, Regularized Linear Regression demonstrated the highest accuracy and interpretability, highlighting its suitability for this dataset. The findings emphasize the potential of machine learning in identifying key predictors of academic performance and offer practical applications for personalized academic advising and time management. This research provides a data-driven framework to support students and educators in optimizing learning outcomes.

This study analyzes the procedures for employee payroll documentation and data transfer at PT PLN Nusantara Power Services to enhance the efficiency and accuracy of human resource (HR) administration. Using a descriptive qualitative method, data were collected through library and field research. The findings indicate that digitalizing HR administration improves operational efficiency, transparency, and compliance with regulations. Digital transformation also minimizes errors and supports data risk management. The author's internship experience highlights the importance of responsibility and accuracy in data management to meet professional standards. This research contributes to the development of technology-based administrative systems in the energy sector.  

Companies often face various obstacles related to managing raw material inventory to meet demand, one of which is Peuyeum Ketan Istimewa. Working in the food processing industry, of course, raw material inventory management needs to be planned optimally to avoid various risks that can harm the company. The Quantity Discount model is used to take advantage of cost savings provided by suppliers when purchases are made in large quantities, while other efforts that can help manage raw materials in a company are by knowing the safety stock and reorder point of raw materials and also forecasting demand to predict future demand. This study will use the Quantity Discount model which optimizes inventory levels by considering storage costs, ordering costs, and quantity discounts. The calculations carried out are also to find the value of the company's Safety Stock and Reorder Point. The results of this study indicate that the use of the Quantity Discount method can reduce total costs by Rp26,319,267/year, while forecasting using the seasonality method increases the accuracy of demand predictions, thus enabling more efficient inventory management. The implementation of this model is expected to provide a significant contribution to operational efficiency and cost reduction at Peuyeum Ketan Istimewa

This research aims to analyze the determination of the cost of production at the Akar coffe Shop to determine the optimal product selling price. In facing intense competition in the coffe industry, determining the right selling price is very important for business continuity. The research method used is mix method approach with production cost analysis, which includes raw material, labor and overhead costs. The findings of the study indicate There is a difference in the calculations according to the simple approach and full costing which affects the selling price.This research emphasizes the importance of accuracy in pricing to maintain a balance between costs and profitability.

In today's world of retail and technology, competition is fiercely competitive. With the development of retail businesses increasing in number and mushrooming in a region, consumer needs are increasing, and retail business players are competing to develop their businesses by utilizing existing technology. Daily sales transaction data continues to increase, causing a lot of storage. Toko Ira has more than 228 sales transaction data records from 2023 to 2024 that have not been used. Data requires a lot of storage space. Additionally, the data has not been used in an effective way. Based on this problem, this research aims to use data mining to classify sales transaction data to determine which items are selling best. This research is a case study with a qualitative approach. This research was conducted with the Naive Bayes method and Rapidminer was used. The results of the sales transaction data classification research are the division of products into best-selling and non-selling categories. The results of this research show that the K-Nearest Neighbors (KNN) algorithm with a 50:50 data division is more effective in predicting and classifying sales of best-selling and non-selling products in IRA stores. The results show that the Naive Bayes algorithm has an accuracy of 89.91%, while the K-Nearest Neighbors (KNN) algorithm has an accuracy of 60.09%.

This research aims to analyze the influence of the application of information technology on increasing the efficiency of business processes in the banking sector. In an increasingly competitive and dynamic business environment, operational efficiency is one of the keys to success for banks. The application of information technology through various digital systems, such as Internet Banking, Mobile Banking, and cloud computing-based platforms, has been proven to speed up transactions and reduce operational costs. Apart from that, automating business processes through ERP and CRM also increases accuracy and makes data-based decision making easier. However, the implementation of this technology faces challenges, such as limited technical skills and organizational cultural resistance. However, proper use of IT can increase productivity, customer satisfaction and strengthen the bank's competitive position. This research provides insight into the importance of IT investment in supporting digital transformation and operational efficiency in the banking sector.

This study discusses the design of a goods storage information system at the Medan Class A Search and Rescue Office to improve the efficiency and accuracy of goods management management. This system is designed to automate the process of storing and tracking goods, which has been done manually and often causes various problems such as tracking difficulties and data inaccuracies. The results of the implementation show that this information system is able to minimize errors, reduce the risk of loss of goods, and ensure the availability of important equipment in real-time. Despite challenges such as user resistance and resource requirements, careful planning and proper training overcame these obstacles. In conclusion, this system contributes significantly in improving the operational performance of the Medan Class A Search and Rescue Office.

Financial management is one of the key factors in determining the success of a company, especially for Micro, Small, and Medium Enterprises (MSMEs) in the digital era. This study aims to analyze the literature that discusses the influence of financial management practices on MSME performance, considering the role of digital technology in financial management. This study explores key aspects of financial management, such as financial planning, budget control, cash flow management, and investment, and how the application of technology can improve its efficiency and effectiveness. The results of the study show that the use of digital technology, such as cloud-based accounting applications and digital payment systems, can improve the accuracy and speed of financial management, which ultimately has a positive impact on MSME performance. This study provides important insights for MSME actors and policy makers in integrating digital technology to strengthen financial management and improve business competitiveness.  

A Critical nursing care is a process for problem solving, decision making as well as an organized and systematic approach to nursing problems. Nursing care must be carried out completely and accurately because it is the nurse's self-defense against demands as well as proof that the correct nursing care has been carried out. The research objective is to find out the description of the implementation of critical nursing care in Emergency Room of Santa Elisabeth Hospital Medan 2023. The research design used is a descriptive research design. There are two populations in this study, namely nursing actions and documentation of critical nursing care, the sampling technique used purposive sampling 51 respondents and total sampling (critical nursing care documentation) as many as 68 respondents. The data collection instrument uses observation sheets for nursing actions consisting of infusions, ECG usage and oxygen therapy, as well as critical nursing care using observation sheets consisting of assessment, diagnosis, intervention/implementation and evaluation. The results show that the infusion was according to the SOP, the use of ECG was according to the SOP and sufficient oxygen therapy is according to the SOP for 51 respondents (100%). Critical nursing care complete review 65 (96%), diagnosis quite complete 48 (71%), complete intervention/implementation 65 (96%), complete evaluation 67 (99%) and documentation of critical nursing care majority complete 65 (96%) of 68 respondents. It is expected to be able to improve the accuracy and completeness of nursing care documentation through training for each officer.

A Critical nursing care is a process for problem solving, decision making as well as an organized and systematic approach to nursing problems. Nursing care must be carried out completely and accurately because it is the nurse's self-defense against demands as well as proof that the correct nursing care has been carried out. The research objective is to find out the description of the implementation of critical nursing care in Emergency Room of Santa Elisabeth Hospital Medan 2023. The research design used is a descriptive research design. There are two populations in this study, namely nursing actions and documentation of critical nursing care, the sampling technique used purposive sampling 51 respondents and total sampling (critical nursing care documentation) as many as 68 respondents. The data collection instrument uses observation sheets for nursing actions consisting of infusions, ECG usage and oxygen therapy, as well as critical nursing care using observation sheets consisting of assessment, diagnosis, intervention/implementation and evaluation. The results show that the infusion was according to the SOP, the use of ECG was according to the SOP and sufficient oxygen therapy is according to the SOP for 51 respondents (100%). Critical nursing care complete review 65 (96%), diagnosis quite complete 48 (71%), complete intervention/implementation 65 (96%), complete evaluation 67 (99%) and documentation of critical nursing care majority complete 65 (96%) of 68 respondents. It is expected to be able to improve the accuracy and completeness of nursing care documentation through training for each officer.

This research aims to evaluate the effectiveness of advertising policy in Kuantan Singingi Regency using qualitative methods and a phenomenological approach. The policy evaluation theory used is William N. Dunn's model, which emphasizes six success criteria, namely effectiveness, efficiency, adequacy, equity, responsiveness and accuracy. Data was collected through interviews, observation and documentation, and analyzed using interactive data analysis. The research results show that advertising policies have not been fully effective in achieving the expected goals, such as increasing regional income and arranging the aesthetics of the city. Findings indicate the existence of illegal advertisements that hinder tax revenues and public dissatisfaction with the government's response to their complaints. Even though the resource structure is in place, implementor skills need to be improved to achieve better efficiency. Licensing and supervision procedures are also considered inadequate, and policies do not provide equal opportunities for all business actors, especially small businesses. This research provides recommendations for improving future policy implementation, with the hope of increasing effectiveness and responsiveness to community needs.

This study analyzes the evaluation of stunting reduction policies in Kuantan Singingi Regency, Indonesia, using William N. Dunn's policy evaluation theory. Stunting is a serious nutritional problem that impacts children's growth and development, especially in the first 1000 days of life. Although there has been a decrease in stunting prevalence in Riau Province, Kuantan Singingi Regency experienced an increase in stunting rates from 17.8% in 2022 to 23% in 2023, making it the highest in the province. The evaluation was conducted based on five criteria: effectiveness, adequacy, equity, responsiveness, and accuracy. The results showed that despite significant efforts in policy implementation, the target for reducing stunting prevalence has not been achieved, with communication between officers and the community still lacking. There is a shortage of skilled personnel and funds, and unequal access to health services. The policy provides a clear framework, but information about the program is still lacking in the community. The assumptions underlying the policy are quite strong, and positive impacts are starting to be seen, but challenges such as lack of training for officers remain. Overall, despite progress, there are still many challenges that need to be addressed to achieve the goal of reducing stunting effectively.

This community service activity aims to implement a digitalization system using BizSchool as a solution to enhance operational efficiency. The methods used include needs observation, software installation, employee training, and system effectiveness evaluation. The implementation results show improved accuracy in cash flow and inventory recording, reduced human errors, and accelerated financial reporting processes. Bizschool also supports deeper data analysis, enabling companies to develop more effective business strategies. This activity recommends utilizing additional features for further integration and regular monitoring to ensure the sustainability of the implementation results. Operational digitalization with systems like Bizschool can serve as a solution for SMEs to increase competitiveness in the market.

This study aims to classify chicken eggs based on their physical size using the concept of computer vision and image segmentation techniques. Compared to the standard methods that have been used so far, this alternative technology is expected to help standardize measurements, cost efficiency, and work effectiveness. In this study, the classification of chicken eggs was carried out using image segmentation and regression analysis. Thus, it is expected that the classification of chicken eggs will have increasingly accurate values. After the image is taken using a webcam, the image segmentation process is used to divide the image into homogeneous areas based on the RGB (true color) color intensity similarity standard. Regression analysis is used to study and measure the relationship between the number of pixels and the weight of the object. The number of pixels indicating the area of ​​the object is the result of image segmentation, which will be entered into the regression equation to calculate the weight (grams). The results showed that the color characteristics of chicken eggs have a normalization of R at least 0.41 and a normalization of G at least 0.3. In addition, the classification test has an accuracy of 100% (36/36) and a weight estimation accuracy of 42 percent (15/36).

This study aims to analyze the sentiment of iPhone product reviews fromAmazon using the BERT (Bidirectional Encoder Representations from Transformers) model to classify reviews as either positive or negative. The dataset, sourced from Kaggle, includes text reviews and star ratings, where high ratings indicate positive sentiment and low ratings indicate negative sentiment. After text preprocessing steps, including data cleaning, tokenization, and sentiment labeling, the BERT model was fine-tuned for sentiment classification, with the data split into training, validation, and test sets. Evaluation results demonstrate that the BERT model achieves a high classification accuracy, with an accuracy rate of 93.9% and a balanced F1 score between precision and recall. Confusion matrix evaluation also indicates that the model consistently identifies both positive and negative sentiments. This study shows that Transformer-based models like BERT are highly effective in understanding customer opinions in e-commerce, with broad application potential for data-driven decision-making in marketing strategies and product development.