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.

One of the CPU scheduling methods that is frequently used to reduce waiting time and average execution time is Shortest Job First (SJF). However, this algorithm's accuracy is largelbravy dependent on how well the job execution time is predicted. The purpose of this study is to enhance work execution time estimates by optimizing the SJF algorithm through the use of the Random Forest Regression model. The model in this study is trained using historical job data. The test results demonstrate how Random Forest Regression may be included into SJF to greatly increase system efficiency, especially in terms of throughput and waiting time reduction.

Innovation in the education sector has become essential with the advancement of digital technology, creating various new platforms and applications to enhance the quality of learning. This study aims to identify the role of Evaloexam in driving innovation in the education sector and fostering a spirit of technological entrepreneurship. The methodology used is quantitative research with a survey approach, involving educators and students who use Evaloexam in high schools (SMA). A total of 100 respondents were randomly selected, and data was collected through online questionnaires over one month. Data analysis was conducted using descriptive and inferential statistics. The research findings show that Evaloexam significantly contributes to facilitating the educational evaluation process and encouraging innovation in new ways of assessing and analyzing student learning performance. This application also sparks interest in technological entrepreneurship among students and educators. The research results also match the findings of previous studies that support the integration of technology in education for improved performance and efficiency. The practical implications of this research are that Evaloexam and similar applications can be more widely integrated into the education system to support innovation and the technological entrepreneurship ecosystem. However, this study is limited to a small sample and one type of evaluation application with a limited data collection period. For future research, it is recommended to involve a larger sample size, a longer duration, and to compare various evaluation applications to obtain more comprehensive insights.

PT Jadi Kaya Raya Bersama, which was founded in 2024 in Banyumas , Indonesia, focuses on providing solution recording reliable finance? for Micro , Small and Medium Enterprises (MSMEs) through fintech applications . Although application This own potential big , a number of problem technical hinder its performance , including disruption in WhatsApp Bot API, error authentication users , and the lack of integration with system banking as well as digital wallet . Problems This influence accuracy recording transactions , efficiency operational , and user data security . For identify reason problems and provide the right solution , conduct a system audit with use COBIT framework . The audit results showed that API disruptions are caused by instability network and errors configuration , while problem authentication related with weak login mechanism . Inability application For integrated with service banking and e-money are obstacles comfort users . Therefore that , it is recommended For do API optimization , improving system authentication with authentication two factor (2FA), as well as develop integration with service banking and e-money. Improvements This expected can increase efficiency , safety and quality service PT Jadi Kaya Raya Bersama fintech application , which in the end support growth of MSMEs in Indonesia.
 

The development of mixed-media technology has produced a variety of high-quality 3D animation techniques. This study examines the performance of three rendering techniques—Eevee, Cycles, and Radeon ProRender—based on rendering speed, visual quality, and memory efficiency. Tiga show 3D is rendered using the aforementioned ketiga technique and is compared in terms of speed, record size, bayangan, cahaya effect, and warning ketetapan. As a result, Eevee is unggul in kecepatan, while Cycles has the best visual quality, albeit being more lambat. Radeon ProRender provides impressive results, but it also excels in bayangan quality and cahaya effect. The use of Blender is recommended based on memory efficiency, graphic quality, or speed priorities.

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.

As a resource supporting public health services, management of electromedical equipment must be carried out quickly, accurately and integratedly so that function, safety, security and benefits can be optimized. The management of electromedical equipment is regulated in the Republic of Indonesia Minister of Health Regulation Number 65 of 2016 concerning Electromedical Service Standards. The expected result of this research is an expert system that can accurately detect damage to sterile electromedical equipment, especially autoclaves. This expert system can then assist electromedical technicians in finding damage and as an assessment in making decisions for appropriate and validated actions to be taken. From the results of testing and analysis of the expert system for detecting damage to Autoclaves using the fuzzy logic method, the following conclusions were obtained. The damage detection expert system application in the Autoclave has been proven to be able to provide 100% diagnosis results. This system can assess the degree of damage of 11.6235981%. based on the input symptoms provided, thus providing decisions that are close to the actual conditions. The expert system built is able to speed up the damage detection process compared to manual methods. This helps in taking quicker action to prevent further damage to the autoclave.

This qualitative literature review explores the realm of histogram-based image forensics for recaptured image detection, addressing the challenges posed by advancements in display technology and the subsequent need for robust forensic techniques. The research methodology involves a systematic approach, including defined research objectives, thorough literature search, data extraction, thematic analysis, and ethical considerations. The focal point is the proposed method utilizing Local Ternary Count (LTC) histograms normalized from residue maps, demonstrating exceptional performance across various databases. The methodology involves residue map calculation, LTC histogram extraction, and experiments showcasing the method's efficiency in both single and mixed databases. The discussion emphasizes emerging frontiers in recaptured image forensics, presenting innovative algorithms categorized by the medium used during the recapture process. The shift towards deep learning methods is noted, with a focus on a proposed algorithm for detecting images recaptured from LCD screens based on quality-aware features and histogram features. The RID field has witnessed advancements, with a detailed overview of methods categorically addressing recapture from LCD screens. Ethical considerations are integrated into the discussion, and the conclusion emphasizes the need for constant adaptation, innovation, and collaboration in the fight against evolving manipulation techniques. Looking ahead, the fusion of features, standardized datasets, and advanced deep learning architectures are identified as key elements for future research in ensuring image authenticity

As 5G networks become more widespread, securing data transmission is increasingly crucial. This paper provides a comparative analysis of cryptographic algorithms in the context of 5G, focusing on security, computational efficiency, and resilience to attacks. The study evaluates the effectiveness of popular encryption algorithms, such as AES, RSA, and ECC, under the unique constraints of 5G. Results demonstrate which algorithms offer the best balance of security and performance, presenting insights that can guide future implementations of secure 5G networks.