The application of Virtual Reality (VR) based on the Metaverse in education is an innovation that can enhance the quality of learning by providing a more interactive and immersive learning experience. This article discusses the development of VR-based learning integrated with the Metaverse using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). Through the stages of analysis, design, development, implementation, and evaluation, VR and Metaverse-based learning can be tailored to students' needs, creating a more flexible and collaborative learning experience. The use of this technology allows students to learn independently or in groups, while improving practical skills and creativity. This study shows that by applying the ADDIE model, VR and Metaverse-based learning can be more effective in increasing student motivation and learning outcomes, addressing the challenges of education in the future.

The e-guestbook application is an application used to record and process guest data arriving at an institution. The quality of the e-guestbook application system needs to be measured to determine the level of quality and user satisfaction with the application. This study aims to analyze the quality measurement of the e-guestbook application using the Six Sigma method at the South Sumatra High Prosecutor's Office. The Six Sigma method was chosen because it can identify and measure Critical to Quality (CTQ), thereby improving the quality of the application. The research results show that the sigma value of the e-guestbook application is 4.16, which means the quality of the application is very good. However, there are several aspects that need improvement, namely the ease of use of the application, the accuracy of the information, and the availability of application features. The recommended improvements are enhancing the user interface, validating input data, and adding a report printing feature. The application of the Six Sigma method has proven effective in measuring and improving the quality of the e-guestbook application at the High Prosecutor's Office of South Sumatra

Due to the variety of types of flowers that exist and having and tracking each variety, making plant lovers and cultivators difficult to distinguish in determining the type of flower, it takes a very long time to find out the type of flower if you only rely on the five senses. With the application of the K-Nearest Neighbor algorithm and feature extraction of color and texture, it is very helpful in image processing to identify flowers more easily and shorten the time, with the greatest accuracy of 71% using the K-7 value, the flower was successfully carried out.

Hotel booking cancellations pose significant challenges to the hospitality industry, affecting revenue management, demand forecasting, and operational efficiency. This study explores the application of machine learning techniques to predict hotel booking cancellations, leveraging structured data derived from hotel management systems. Various classification algorithms, including Random Forest, XGBoost, and LightGBM were evaluated to identify the most effective predictive model. The findings reveal that XGBoost model outperforms other models, achieving F2-score of 0.7897. Key influencing factors include deposit type, total number of special requests, and marketing segment. The results underscore the potential of predictive modeling in optimizing hotel revenue strategies by enabling proactive measures such as dynamic pricing, targeted customer engagement, and improved overbooking policies. This study contributes to the ongoing advancements in data-driven decision-making within the hospitality industry, offering insights into how machine learning can mitigate financial risks associated with booking cancellations.

Students reading interest is a crucial factor in enhancing the quality of education. However, the lack of structured data makes it challenging to identify specific patterns of reading interest. This study aims to implement a data mining method using the Naive Bayes algorithm to analyze students' reading interest at SMP Negeri 2 Palembang's library. The data used includes book borrowing history, types of books, and library visit frequency over one semester. The analysis results indicate that the Naive Bayes method achieves an accuracy rate of 80% in classifying reading interest based on predetermined categories. These findings are expected to assist the school in designing more effective literacy programs.
 

Digitalization is the main pillar of change in various aspects of life, including education and social interaction. Balai Diklat Keagamaan Palembang has adopted digital transformation through the development of eL TV, a digital learning platform. This research aims to analyze the User Interface (UI) and User Experience (UX) on the eL TV website of the Palembang Religious Education and Training Center from an anthropological perspective using the Design Thinking method. This approach integrates aspects of culture, habits, and user behavior to create socially and contextually relevant designs. The research stages include Empathize through observations and interviews to understand user needs, Define to identify key problems, Ideate to generate creative solutions, Prototype to build an initial design that is responsive to local values, and Test to measure the effectiveness of the new design. This research concludes that an anthropological approach to UI/UX design through the Design Thinking method can increase user relevance and satisfaction with the eL TV website, while encouraging the use of technology that is more inclusive and oriented to the needs of local communities.
 

Efficient energy use in household appliances has become a primary concern as awareness of environmental sustainability grows. Smart electric stoves with automatic temperature control features offer an innovative solution to enhance energy efficiency while ensuring comfort and safety during use. This study aims to design and develop a smart electric stove system based on a microcontroller with automatic temperature control algorithms. The system employs temperature sensors to detect real-time temperature changes and a Proportional-Integral-Derivative (PID) algorithm to maintain stable temperatures as set by the user. A simple and intuitive user interface is designed to facilitate operation, while the automation feature allows the system to shut off the heating element independently when the temperature exceeds a safe threshold. Test results demonstrate that the designed system can reduce energy consumption by up to 25% compared to conventional electric stoves without automatic controls. Therefore, this research significantly contributes to the development of energy-efficient and safe smart kitchen appliances.

This study aims to analyze data security in popular chat applications using network forensics methods. This method enables the collection and analysis of data from networks to detect and understand potential threats or security breaches. In this research, several chat applications are analyzed to identify vulnerabilities that unauthorized parties could exploit. The results of this study are expected to provide a better understanding of the steps that can be taken to enhance data security in chat applications.
 
 

This literature review discusses the application of Agile methodology in modern software development. Agile offers a flexible, iterative approach that contrasts with traditional waterfall models. The objective of this study is to provide insights into how Agile frameworks such as Scrum, Kanban, and Extreme Programming have influenced development speed, software quality, and team collaboration. Methodologically, the study synthesizes findings from recent academic and industrial literature from the last five years. The results show that Agile enhances adaptability and customer satisfaction, although challenges remain in large-scale implementations.
 

This research uses a deep learning-based sentiment analysis approach with several main stages, namely data collection, preprocessing, model preparation, and model building. In addition, this research also evaluates the impact of data splitting techniques on the model's performance during the training process.The evaluation results show that the LSTM-GRU model achieved the best performance on the character aspect, with an F1-score of 0.72 in the 90:10 splitting scenario. Meanwhile, the lowest F1-score was found in the editing aspect, with a value of 0.51 in the 80:20 splitting scenario. These findings indicate that the model is more effective in recognizing sentiment in narrative aspects compared to technical aspects. Furthermore, the data splitting technique significantly influences model performance, both in determining accuracy levels and in optimizing the model's effectiveness in identifying sentiment patterns more accurately.