Booking is an agreement process in the form of ordering goods or services. The Youth and Sports Service (DISPORASU) is a government agency under the auspices of the Governor of North Sumatra. DISPORASU provides sports infrastructure that can be used by the general public by rental method. In the rental process, DISPORASU still applies the direct rental method, by coming to the DISPORASU office in Medan City, so this results in a large number of tenants piling up at the same time. Therefore, a system is needed that helps tenants in carrying out the infrastructure rental process. The concept is an Android-based Sports Infrastructure E-Booking application. This application was built and designed using the waterfall model and UML (Unified Modeling Language) with several diagrams, namely Use Case Diagrams, Activity Diagrams and Class Diagrams. The programming language used is Dart, Flutter Framework and Firebase as the database. By building this sports infrastructure E-Booking application, it is hoped that it can help improve performance in future business processes and become a reference for employees/staff in this agency itself.

Image classification is a crucial technique in digital image processing, used in various applications such as object recognition, surveillance systems, and medical image analysis. This research explores the use of gradient magnitude-based edge detection and Robert's Cross methods in improving the classification accuracy of grayscale images. Edge detection is used to identify object boundaries, while gradient magnitude amplifies intensity differences, thus clarifying existing patterns. Through experiments conducted on grayscale images, the results show that this method is able to detect edges with significant accuracy. The gradient values obtained from the combination of Rx and Ry matrices give a strong indication of the presence of edges, which plays an important role in image classification. With an accuracy of 75%, the method proved to be effective, although there are still challenges in dealing with images with high noise or low contrast. The conclusion of this study shows that the combination of edge detection and gradient magnitude is a promising approach for image classification, providing results that can be applied in various domains, including medical and surveillance. Further research is recommended to optimize this approach and extend its application to more complex datasets.

This study presents the development and implementation of a Patient Management System (PMS) at Noura Aesthetic Clinic using the Fuzzy Multiple Attribute Decision Making (FMADM) method with Simple Additive Weighting (SAW). The aim is to enhance the decision-making process for patient treatment prioritization and management. The PMS integrates various patient attributes, including medical history, treatment urgency, and resource availability, into a comprehensive decision-making framework. By employing the FMADM method, the system addresses the inherent uncertainties and subjectivities in patient data, ensuring more accurate and reliable prioritization. The SAW technique further refines this process by assigning weighted scores to each attribute, facilitating a straightforward and effective comparison. This combination allows for a balanced assessment of multiple factors, promoting optimal resource allocation and improving overall patient care. The implementation at Noura Aesthetic Clinic demonstrated significant improvements in operational efficiency and patient satisfaction. The system's adaptability to diverse clinical settings and its user-friendly interface make it a valuable tool for healthcare providers. This study underscores the potential of advanced decision-making methodologies in transforming patient management practices, paving the way for more informed and equitable healthcare delivery.

The quality of the software is greatly determined by the testing phase, which involves various test cases that can be conducted through manual testing and automation testing. Manual testing is performed manually without using automation scripts, whereas automation testing is conducted using automation scripts. ABC is a company that operates globally in the field of access control, with the Card Printer being one of the menus used in access control. In the development process of this software, both manual and automation testing phases are carried out. The automation testing process employs the Selenium tool, which has proven to be time-consuming and poses challenges when running numerous test cases. This research aims to develop automation testing using Playwright to address the long execution time issue encountered with Selenium. The research utilizes the Card Printer project in the development of automation testing and adopts the Agile methodology. The result of developing automation testing using Playwright was successfully applied to 12 test cases. Additionally, the time analysis between Playwright and Selenium showed that Playwright has a total execution time of 4.9 minutes, which is faster compared to Selenium's total execution time of 8.3 minutes. With faster execution times, Playwright can be considered a tool in the development of automation testing.

Flood can be interpreted as an event that occurs suddenly and quickly enough where the water discharge in the drainage channel cannot be accommodated, so that the blocked area causes the water discharge in the drainage channel in several surrounding areas to overflow and is one of the natural disasters that occurs at an unexpected time and cannot be prevented, because of this, a prediction must be made to detect floods for the next day. Flood prediction is a crucial aspect of disaster management and mitigation, particularly in flood-prone areas such as Jakarta, Indonesia. This study aims to leverage Support Vector Regression (SVR) to predict flood events by analyzing various environmental and hydrological factors that influence flooding. The primary data sources include historical wheater data, river water levels, floodgate positions in Jakarta. The data preprocessing involved cleaning, handling missing values, and normalizing the datasets to ensure compatibility with the SVR model. Feature selection was conducted to identify the most relevant predictors of flooding, such as wheater data, and river water levels. The dataset was then split into training and testing sets, maintaining an 80-20 ratio to ensure robust model validation. An SVR model with a radial basis function (RBF) kernel was trained on the standardized training data. The model's performance was evaluated using Root Mean Squared Error (RMSE) as the primary metric. The RMSE produced in this study was 0.112 with an R Square accuracy of 0.977. The results indicated that the SVR model could effectively predict flood events with a reasonable degree of accuracy, demonstrating its potential as a valuable tool in flood forecasting.

Schools as educational institutions with information technology services in the form of New Student Admission Systems, e-report systems, and other information technology services require devices supported by good service. Information technology devices must be appropriately managed to conduct business processes according to business goals. With good service management, the business value offered to customers (students) will increase. ITIL V4 contains information technology service management guidelines that contain best practices for information technology service management, including attention to customer experience, value streams, and support for current digital transformation. This research measures information technology equipment services at high schools in Kendal Regency. This research uses indicators adapted to schools with ITIL V4: Key Performance Indicators (KPI) and Critical Success Factors (CSF). The results of this research show that the performance of the hardware needs to be improved, and the value of the maturity model for each service device is still not good.

Currently, mental health is an unresolved mental health problem both at the national and international levels. Mental health disorders are conditions where a person has difficulty in adjusting to the conditions around them. Mental Health is an important aspect of overall health. Efforts to maintain and improve it can help a person achieve better well-being in everyday life. This research aims to conduct Early Prediction Analysis related to mental health problems experienced by students by measuring the accuracy level of the analysis. This research was conducted using the CART (Classification and Regression Trees) and KNN (K-Nearest Neighbor) algorithms with a set of Mental Health Datasets consisting of 11 attributes and 101 data. The data is processed using the Weka Application and the accuracy results of each algorithm are obtained, amounting to 94.0594% for the CART Algorithm and 91.0891% for the KNN Algorithm. From this achievement, it can be concluded that the performance of the CART and KNN algorithms falls into the Excellent Classification category. Judging from the accuracy obtained, the CART algorithm has a higher accuracy value than the KNN algorithm, so the CART algorithm has a high performance for analyzing early prediction of mental health of students who do not take steps in seeking mental health support.

This research was conducted at the Jambi Province Public Works and Public Housing Office (PUPR). This research is entitled "Design of a Website-Based Archiving Information System at the Jambi Province Public Works and Public Housing Office (PUPR)". The purpose of this research is to document the process of designing an information system that functions to provide information to the PUPR Office of Jambi Province. This research was conducted qualitatively by conducting interviews and observations with the PUPR Office of Jambi Province to complete the research. The document filing system of the Jambi Province PUPR Office was designed using the prototype method. As a result of the analysis and observations made, it can be concluded that one effective way to facilitate the work of employees in finding documents is to build a web-based archiving information system. This system is expected to produce information quickly, precisely, and accurately without reducing the value of the information itself.

This research analyzes the application of the Multi Objective Optimization by Ratio Analysis (MOORA) method model in determining the Pilot Area at PT XYZ. This method is used to evaluate various performance criteria, including customer satisfaction, productivity, service quality, and operational efficiency. Currently, the Pilot Area assessment and selection process at PT XYZ is still done manually, which causes a lack of accuracy and efficiency. MOORA was chosen for its ability to handle multi-criteria decision-making problems more systematically and objectively. The analysis results showed that Alternative Area 7 obtained the highest final score of 0.39, placing it as an area with superior performance. The application of MOORA is proven to improve accuracy and efficiency in the Pilot Area determination process, providing a more objective basis for decision-making. By using MOORA, PT XYZ can evaluate area performance more comprehensively and accountably. This research recommends that PT XYZ implement the MOORA method thoroughly and conduct periodic evaluations of the methods used. For theory development, PT XYZ can add specific evaluation criteria according to company needs. The implementation of these suggestions is expected to improve the quality of service and competitiveness of PT XYZ in the global market. Further research is expected to compare MOORA with other methods to strengthen the validity of the results. Thus, this research not only provides a practical contribution to PT XYZ but also adds academic insight into the application of multi-criteria optimization methods in the context of performance management and service improvement.

Technical analysis stands as a pivotal strategy in analyzing graphic patterns to forecast future movements in crypto asset prices. However, comprehending numerous patterns poses a significant challenge for novice investors venturing into the investment realm. This study aims to facilitate investors in recognizing crypto price graph forms by classifying cryptographic price chart patterns. The dataset comprises images of seven types of crypto price graphic patterns obtained from the Kagle website, totaling 210 data points. A 70:30 training and testing data split is employed to ensure robust model evaluation. The study explores nine different Histogram of Oriented Gradients (HOG) parameter combinations for graphic pattern extraction. Leveraging the artificial neural network (ANN) classification method with parameter hyper tuning, the study assesses various HOG parameter configurations to optimize classification performance. The most optimal results are achieved with parameters Bin = 9, Cell Size = 16x16, and Block Size = 1x1, boasting an accuracy rate of 95.23%, precision of 95.55%, and recall of 95.23%. This classification approach streamlines the process for investors, enabling them to discern crypto price graph patterns effectively, thereby enhancing their investment decision-making capabilities in the dynamic cryptocurrency market landscape. By providing a structured method for pattern recognition, this study contributes to democratizing access to technical analysis tools, particularly benefiting novice investors seeking to navigate the complexities of cryptocurrency investment.