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.

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.

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.
 

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.

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 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.

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.

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.

The rapid adoption of cloud computing has transformed the way organizations manage and store their data. However, this shift has also increased vulnerabilities to cyber threats. Anomaly detection is a critical component of cybersecurity frameworks, allowing for the identification of unusual patterns that may indicate security breaches. This paper presents a comprehensive framework for anomaly detection in cloud computing environments. It reviews existing methodologies, explores the integration of machine learning techniques, and discusses the challenges associated with implementing these systems. The proposed framework aims to enhance the cybersecurity posture of organizations by providing proactive detection of anomalies.

The detection of computer network attacks is becoming increasingly important as the complexity of cyber-attacks threatening information systems and infrastructure continues to rise. To address these threats, artificial intelligence techniques have become a primary focus in the development of more effective attack detection systems. One algorithm that has proven reliable in this context is the C4.5 decision tree. This study aims to apply the C4.5 algorithm in network attack detection using a dataset that includes various types of attacks and network activities. The process includes data preprocessing, decision tree model building, and model performance evaluation. The results show that the C4.5 decision tree algorithm is effective in classifying network activities into attacks and normal activities with a satisfactory level of accuracy. The model successfully recognizes attack-related patterns, and further analysis identifies key factors influencing attack detection. This research provides a significant contribution to the development of reliable and efficient attack detection systems in computer networks. By applying the C4.5 decision tree algorithm, it is expected to help enhance information security and protect network infrastructure from increasingly complex cyber threats

As cyber-physical systems (CPS) gain prevalence in sectors such as manufacturing, transportation, and critical infrastructure, ensuring their security and reliability is paramount. Traditional anomaly detection methods often fall short due to the dynamic and complex nature of CPS, leading to missed or false alarms. This study introduces an enhanced machine learning model that integrates statistical and deep learning techniques for real-time anomaly detection in CPS. By employing a hybrid approach of convolutional neural networks (CNNs) with statistical pattern recognition, the model demonstrates improved detection accuracy and responsiveness. Performance is evaluated using industry-standard CPS datasets, showing that the proposed model outperforms existing techniques in both accuracy and efficiency.

With the exponential growth of social media platforms, vast amounts of data are generated daily, capturing public opinions, sentiments, and trends in real time. Automatic sentiment analysis using Natural Language Processing (NLP) has emerged as an essential tool to process this data, helping industries, researchers, and policymakers understand social sentiment more effectively. This study explores various NLP techniques for sentiment analysis, including machine learning-based, lexicon-based, and deep learning models. By examining advancements in NLP algorithms and challenges related to language diversity, slang, and context in social media data, this paper highlights the strengths and limitations of current methodologies and discusses potential future directions.

The church plays a crucial role in spreading Christian values and nurturing the growth of faith within its community. However, in the context of GPSI EFATA, there are several challenges in effectively conveying church information. These challenges include the unappealing presentation of physical congregation news, the accumulation of paper, and limited distribution of information. In order to address these issues, this research project utilizes the Design Thinking method to develop a smartphone-based application for congregational news. The Design Thinking method consists of five stages: empathy, definition, ideation, prototype, and testing. During the empathy stage, the needs of the congregation are identified, particularly the need for more accessible and engaging information. The ideation stage then generates a solution in the form of a smartphone application. This application includes various features such as notifications, prayer schedules, financial reports, photo galleries, and management profiles. A prototype of the application was developed to meet these needs and was subsequently tested using a questionnaire. The results of the testing indicated that the application had an easy-to-understand interface, adequate features, and received suggestions for improvement. Overall, this smartphone application offers a modern and efficient solution to the challenges of delivering information within churches. It enhances accessibility and improves the quality of communication with the congregation.
                                      
 

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.

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

The proliferation of the Industrial Internet of Things (IIoT) has transformed manufacturing, energy, and logistics, generating vast amounts of data that demand efficient processing solutions. Edge and cloud computing have emerged as two main architectures that can support IIoT applications by addressing latency, bandwidth, and computational challenges. This study presents a comparative analysis of edge and cloud computing architectures in the context of industrial IoT, focusing on performance, scalability, security, and cost. By analyzing case studies from manufacturing, logistics, and energy sectors, we identify the strengths and limitations of each approach, providing recommendations for selecting optimal architectures based on application needs.

Data centers are major contributors to global energy consumption, with significant implications for operational costs and environmental sustainability. As energy demand increases, optimizing energy usage within these facilities has become essential. This study investigates the application of machine learning-based predictive analytics to enhance energy efficiency in data centers. By leveraging models such as Random Forest, Neural Networks, and Deep Learning, predictive analytics forecasts energy demands based on variables like temperature, workload, and time of day. Results from multiple case studies reveal that machine learning models can reduce energy consumption by up to 20%, offering a sustainable solution without compromising data center performance.

Health information systems face major challenges in terms of data security and integrity. This article proposes a blockchain-based framework that enables secure data sharing among various entities in a health information system. The framework ensures the confidentiality, integrity, and transparency of patient data through the use of smart contract technology and hash-based encryption. Case studies on several hospitals demonstrate improved data security without sacrificing system efficiency.

Customer sentiment analysis provides valuable insights for ecommerce businesses, but traditional methods often fall short in handling complex and contextrich language. This paper explores the use of Natural Language Processing (NLP) techniques, including BERT and transformer models, to improve sentiment analysis accuracy in ecommerce. The study compares the performance of different NLP models in capturing nuanced customer sentiment from online reviews. Findings indicate that advanced NLP techniques substantially increase accuracy and offer practical applications for improving customer experience and business strategy in ecommerce.Keywords: Natural Language Processing, sentiment analysis, ecommerce, customer experience, BERT, transformer models.

The development of Virtual Reality (VR) technology opens up new opportunities to improve the quality of online learning through more interactive and immersive learning experiences. This study aims to design the user interface (UI) and user experience (UX) of VR-based online classes using the Design Thinking method to meet students' needs in a virtual learning environment. The Design Thinking method, which includes the stages of empathy, definition, ideation, prototype, and testing, is applied to produce a user-friendly interface design that supports student engagement and social interaction during the learning process. The results of the study show that the VR-based interface is able to increase student engagement through more active interactions, intuitive navigation, and collaboration features that approach interactions in a physical classroom. Testing of the prototype shows that this design can meet students' needs in increasing engagement and learning comfort. It is hoped that this study can be a reference in the development of an effective and efficient VR-based online learning platform.

Smart cities generate vast amounts of data requiring rapid processing to improve urban management. This study investigates the application of edge computing to achieve realtime data processing in smart cities, minimizing latency and enhancing system responsiveness. Through a combination of theoretical analysis and case studies, the paper examines how edge computing can reduce data transmission delays and network congestion. The results reveal that implementing edge computing can significantly improve data efficiency in smart cities, offering a sustainable solution to meet growing urban demands.

The increase in connected IoT devices causes increased vulnerability to cyber attacks. This research develops a hybrid machine learning model to detect real-time anomalies in IoT networks. This model combines the K-Nearest Neighbors (KNN) and Random Forest (RF) algorithms to increase accuracy and efficiency. Evaluation was carried out using the UNSW-NB15 dataset to test model performance. The results show that this hybrid approach is able to detect anomalies with high accuracy and a low false positive rate.

Cloud computing faces challenges regarding data integrity and security due to its centralized nature. This paper proposes a blockchain-based data integrity management system that decentralizes cloud data storage and ensures authenticity and traceability. By leveraging smart contracts, the proposed system validates data transactions, prevents unauthorized alterations, and provides a transparent audit trail. Experimental results reveal that the system can maintain high levels of data integrity with minimal latency and computational overhead, offering a practical solution to enhance data security in decentralized cloud environments.

Industrial Control Systems (ICS) play a critical role in managing infrastructure but are vulnerable to cyber-attacks. This paper presents an AI-driven Intrusion Detection System (IDS) specifically designed for ICS, utilizing a combination of supervised and unsupervised machine learning algorithms. By incorporating real-time anomaly detection and pattern recognition, the proposed IDS identifies potential intrusions while maintaining high accuracy. The experimental results show the system’s effectiveness in detecting cyber threats in real-world ICS environments, providing a scalable solution for enhancing cybersecurity in critical infrastructure.

Geographic information systems (GIS) can be said to be a technology that is a tool that can manipulate, store, analyze and even display natural conditions with the help of data. This study focuses on the utilization of GIS applications and websites. The purpose of this study is to see and find out the utilization of GIS applications and websites in human life. The research method used is literature study. In this literature study, 130 international journals and 100 national journals were obtained with the search year of the last 5 years, namely 2018-2022, which were then studied further. 33 selected articles were produced which were used in writing the article. The results of this study are the utilization of geographic information system applications and websites to help human activities and needs in various sectors, such as in the field of education can map the location of industrial work practice locations (PRAKERIN), the health sector can map the location of pharmacies, the transportation sector can provide information related to routes on public transportation and in other sectors.

Serang Raya University, is a private tertiary institution in the province of Banten, There are 4 faculties, Vocational D3 and 17 Study Programs, in the process of teaching and learning activities each lecturer has a different way of teaching students. Faculty of Information Technology, Computer Science Engineering study program with 30 lecturers, 9 classes, 59 courses and 126 students. This does not require the possibility that with the number of courses taken on campus there are sti ll many students who do not understand what the lecturer is delivering. Management of student comments on the understanding of lecture material is designed to make it easy for students to comment on the lecturer's presentation of the material. This is also used as evaluation material for lecturers regarding the delivery of material. Currently, Serang Raya University does not have a website-based information system. From this discussion, comments are made with the Rule-based Finite State Automata model. In reading the comments, this produces a system that can read comments word by word until the end of the word with a space separator so that it finds keywords, namely the keywords understand and don't understand.

The integration of IT Governance and expert system design offers transformative benefits for enhancing library user services. This research employs the COBIT 5.0 framework to align IT strategies with library objectives while developing an expert system tailored for personalized recommendations. The findings indicate that the expert system significantly improves operational efficiency, service accuracy, and user satisfaction by using user profiles to recommend relevant materials and streamline the borrowing process. Testing revealed high user satisfaction levels, with 96.6% finding the system effective and 100% confirming its efficiency. Additionally, IT Governance ensures strategic integration between technological infrastructure and service quality objectives, enabling data-driven decision-making. The study also highlights challenges, such as the need for robust data management and user training, suggesting areas for future improvement. Recommendations include incorporating machine learning to enhance system intelligence, conducting regular evaluations to maintain system relevance, and testing the scalability of this approach across various types of libraries. By integrating IT Governance with an expert system, this research sets a strong foundation for modernizing library services to better meet user expectations in the digital era.