Perkembangan teknologi informasi yang pesat telah mendorong kebutuhan akan platform pembuatan website yang profesional, terutama bagi bisnis, individu, maupun organisasi tanpa latar belakang teknis. Penelitian ini bertujuan untuk membangun sistem informasi jasa pembuatan website UpSite menggunakan metode prototyping, dengan integrasi fitur business intelligence. Metode ini dipilih karena bersifat iteratif dan memungkinkan penyesuaian sistem secara langsung berdasarkan umpan balik pelanggan. Kebaruan utama dari sistem ini adalah integrasi fitur Business Intelligence (BI) pada dashboard admin, yang menyajikan visualisasi data penjualan dan analisis layanan terlaris secara real-time untuk mendukung pengambilan keputusan. Proses pengembangan mencakup komunikasi kebutuhan, perencanaan awal, pemodelan sistem (use case diagram, activity diagram, dan class diagram), pembangunan prototipe, serta pengujian menggunakan metode black box dan User Acceptance Testing (UAT). Hasil pengujian menunjukkan bahwa seluruh fitur inti, termasuk BI, berjalan sesuai spesifikasi dan memperoleh respons sangat baik dari calon pelanggan. Dengan demikian, UpSite dinyatakan layak digunakan sebagai solusi layanan pembuatan website yang tidak hanya fungsional, tetapi juga inovatif dengan dukungan analitik berbasis data.
 
Kata Kunci - UpSite; Metode Prototyping; Pembuatan Website; Business Intelligence; Pengujian Black Box

Software testing is a critical phase in information system development to ensure the system's quality and reliability. This study aims to evaluate the reliability and functionality of PT Perta Sakti Abadi's financial information system using the black-box testing method with the Equivalence Partitioning (EP) technique. This technique allows input data to be grouped into valid and invalid categories, minimizing test cases without reducing testing coverage. The testing focuses on the login feature as the system's primary component by evaluating various input combinations. The testing scenarios include boundary conditions to ensure the system handles inputs correctly in various situations.The results indicate that the system successfully verifies valid credentials, rejects access with invalid data, and provides informative error messages. Additionally, the system demonstrates resilience in handling testing scenarios, including inputs with special characters and empty fields. Input validation mechanisms function optimally, supporting secure user access and ensuring the login feature aligns with functional specifications. This successful testing forms a strong foundation for testing other modules, such as multi-level authentication and data encryption. Thus, the Equivalence Partitioning technique within the black-box testing method proves effective in enhancing the quality of web-based financial information systems.

In the digital age, efficient sales transaction management is crucial for business success. Point of Sale (POS) systems, such as Wingpos, are a solution for transaction recording, inventory management, sales reporting, and data analysis. However, software errors can disrupt operations and compromise security aspects. This research aims to assess the functionality and quality of the Wingpos website by using the Black Box Testing method, which tests software by comparing actual outputs to expected results based on given inputs. The Equivalence Partitioning technique was applied to focus on testing functional aspects of the login and register features. Through this testing, technical insights into the quality of the Wingpos software were gained, as well as a systematic approach to web-based application testing. The results of the study are expected to improve user experience and serve as a reference in the development of similar systems in the future.

This study focuses on black-box testing of JivaJoy software, an online product stock management and ordering system. The primary goal of this research is to evaluate the functionality and performance of the system's key features, including profile management, CRUD operations for admin and customer accounts, product stock management, shopping cart, order management, and AI counseling. Black-box testing was applied to assess whether these features meet expected operational standards and user requirements without considering the internal code structure. The test results indicate that most features function as expected. However, some issues were identified related to input validation, particularly with email format, phone number length, file uploads, and product stock management. Additionally, the order management and AI counseling features showed deficiencies in error handling and input validation. Based on these findings, the study recommends improvements in input validation, product stock management, shopping cart functionality, and AI counseling systems to enhance the reliability and performance of the JivaJoy platform. These enhancements are essential to deliver more efficient, secure, and optimal services, ensuring better user experiences and customer satisfaction.

Facial Emotion Recognition (FER) is a key technology for identifying emotions based on facial expressions, with applications in human-computer interaction, mental health monitoring, and customer analysis. This study presents the development of a real-time emotion recognition system using Convolutional Neural Networks (CNNs) and OpenCV, addressing challenges such as varying lighting and facial occlusions. The system, trained on the FER2013 dataset, achieved 85% accuracy in emotion classification, demonstrating high performance in detecting happiness, sadness, and surprise. The results highlight the system's effectiveness in real-time applications, offering potential for use in mental health and customer behavior analysis.

This research focuses on evaluating the Combox web application, a digital tool designed to help Food and Beverage (F&B) business owners strengthen their online presence. The analysis was carried out through Black Box Testing, specifically using the Equivalence Partitioning method, to assess core functionalities like login, logout, product management, and pagination. The findings reveal that while most features function as intended, there are issues with product addition and editing, as well as pagination when no data is available. These results highlight areas that need refinement to improve the application’s reliability and user experience. In summary, this research supports the advancement of a digital platform that enables F&B businesses to harness technology effectively in today’s competitive landscape.

The growing number of vehicles in major cities has posed significant challenges in parking lot management. Motorists often have difficulty finding empty parking slots quickly, which not only wastes time but also aggravates traffic congestion and increases air pollution. This research develops a Python-based smart parking system by utilizing the OpenCV library to detect the status of parking slots in real-time. The system uses a camera as the main sensor and processes the image using techniques such as grayscale, Gaussian blur, and adaptive threshold to identify the parking slot status, whether empty or occupied, with good accuracy. The parking slot coordinate data is stored in CSV format to ensure efficient data management. Experimental results with video recordings show that the system is able to operate well in various parking conditions. The system proved to be cost-effective and easy to implement, making it an ideal solution for parking managers who want to improve management efficiency without being burdened with high costs. This research offers a practical solution to help motorists and parking managers optimize parking space usage, reduce search time, and minimize negative impacts such as congestion and carbon emissions.

This study presents the development of an automatic passenger counting system for public buses using YOLOv8 based on Convolutional Neural Networks (CNN). The system detects and counts passengers in real-time to optimize bus capacity and enhance operational efficiency. Results indicate that the system achieves high accuracy in the front camera view (confidence score of 0.82). However, in the rear camera view the accuracy slightly decreases (confidence score of 0.76) due to object overlap, emphasizing the importance of proper camera placement. The system offers potential improvements in bus capacity management, reduced operational costs, and enhanced passenger comfort. These findings contribute to advancing smarter and more efficient public transportation systems.

Traffic accidents are one of the leading causes of death worldwide, where drowsiness while driving is a significant factor that reduces driver alertness. This study develops a real-time driver drowsiness detection system using the Eye Aspect Ratio (EAR) method to avoid this. EAR calculates the ratio of the upper and lower eyelid distances to detect signs of drowsiness based on changes in eye shape. This system utilizes the OpenCV and Dlib libraries to identify faces and measure EAR, with a threshold of 0.25 as a warning trigger. If the EAR value drops below the threshold in several consecutive frames, the system automatically activates an alarm to increase driver alertness. With the advantages of cost efficiency and ease of implementation without additional hardware, this system is suitable for various types of vehicles. The results show that this system is effective in providing early warnings, thus helping to reduce the risk of accidents due to drowsiness.

The development of technology with digital image processing is often utilized to solve various problems in image processing, such as facial recognition, object detection, and interaction between users. In this study, we developed an interactive hand gesture-based game titled "Slap Mosquito" that utilizes image processing techniques to control the game through hand gestures. Using Rapid Application Development (RAD), Python, OpenCV, and Pygame methodologies, this game allows users to slap mosquitoes virtually in real-time through hand gesture recognition that is read by the camera and translated into in-game actions. RAD allows rapid development iterations and improvements based on user feedback, which is essential for improving system responsiveness and accuracy. This study focuses on detection precision, system responsiveness, and the impact of lighting on game performance, as measured using frames per second (FPS) and user gameplay results. The test results show that optimal lighting meets high detection accuracy, while low lighting conditions have a negative impact on accuracy and responsiveness. The results of this study provide insights for further development of gesture-based applications, especially regarding the importance of optimizing technical parameters and RAD methodology in improving user experience.