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Brilliance: Research of Artificial Intelligence
Published by Information Technology and Science
ISSN : -     EISSN : 28079035     DOI : https://doi.org/10.47709
Core Subject : Science, Education,
Decision Sciences, Operations Research & Management Mathematics Other
Brilliance: Research of Artificial Intelligence is The Scientific Journal. Brilliance is published twice in one year, namely in February, May and November. Brilliance aims to promote research in the field of Informatics Engineering which focuses on publishing quality papers about the latest information about Artificial Intelligence. Submitted papers will be reviewed by the Journal and Association technical committee. All articles submitted must be original reports, previously published research results, experimental or theoretical, and colleagues will review. Articles sent to the Brilliance may not be published elsewhere. The manuscript must follow the author guidelines provided by Brilliance and must be reviewed and edited. Brilliance is published by Information Technology and Science (ITScience), a Research Institute in Medan, North Sumatra, Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 544 Documents
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Design of Reservation and Customer Management Information System at Polka Barbershop Using Laravel Framework Almaarij, Muhammad Abrar Farid; Mansyuri, Umar; Arief, Rahadian
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.5914

Abstract

The advancement of digital technology has driven service-based businesses, including barbershops, to implement information systems to enhance operational efficiency. Polka Barbershop, located in Serang City, still utilizes manual methods for managing reservations and customer data, leading to disorganized queues, limited access to digital transactions, and challenges in generating reports. This study aims to design a web-based reservation and customer management information system using the Laravel framework. The system was developed following the waterfall model, encompassing stages such as requirements analysis, system design with UML diagrams, implementation, and testing through Blackbox and User Acceptance Testing (UAT). Key features of the system include online booking, real-time queue management, Midtrans payment gateway integration, and automated report generation. Testing results indicate that the system meets user requirements, improves service speed, and enhances the accuracy of customer data. The findings suggest that implementing a web-based information system can substantially improve service quality and operational efficiency in barbershops, offering a scalable solution for similar businesses.
Designing a Web-Based LMS at SMKN 4 Serang Using Express.js Naufal, Dody; Auliana, Sigit; Aryono , Gagah Dwiki Putra
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6132

Abstract

The rapid advancement of digital technology in the education sector has transformed the learning process to become more efficient, flexible, and accessible. One of the innovations that support this transformation is the Learning Management System (LMS), which facilitates digital-based learning and improves interactions between teachers and students. However, SMKN 4 Kota Serang has not yet implemented an integrated LMS platform, resulting in limited support for Grade XII students in preparing for their final examinations. This research aims to design and implement a web-based LMS application using the Express.js framework to address these challenges and support digital learning needs. The development process follows the Waterfall methodology, which includes several sequential stages: requirement analysis, system design, implementation, testing, and maintenance. Data collection methods include observation, interviews, and literature studies to understand user needs comprehensively. The resulting system offers core features such as digital material management, assignment submission and grading, and a discussion forum to support structured communication between users. With the implementation of Express.js, the LMS application is developed to be fast, lightweight, and scalable, making it suitable for the school's environment. This system is expected to assist both teachers and students in conducting learning activities more effectively and efficiently. Overall, this research contributes to improving the quality of digital learning at SMKN 4 Kota Serang and provides a foundation for further system development in the future.
Technical Performance Comparison of Modern Frontend Frameworks Study on Svelte, React, and Vue Putra, Fauzan Prasetyo Eka; Hasbullah, Hasbullah; Muslim, Farhan; Paradina, Reni
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6133

Abstract

This research aims to analyze and compare the technical performance of three of the most widely used modern frontend frameworks, namely Svelte, React, and Vue. These frameworks are popular among developers for building responsive, efficient, and maintainable user interfaces. Using a systematic and controlled experimental approach, we developed identical simple web applications on each of the three frameworks to ensure fairness in comparison. The evaluation focused on four key performance metrics: rendering time, bundle size, memory usage during idle, and DOM operation efficiency. The experiments were conducted using a desktop computer equipped with an AMD Ryzen 5 5600X processor, 32 GB of RAM, and the Firefox browser running on the Ubuntu 22.04 LTS operating system. The results revealed that Svelte consistently outperformed React and Vue in all measured aspects. It achieved the fastest rendering time at 110 milliseconds, the smallest bundle size of 22 KB, the lowest idle memory usage at 7.8 MB, and the most efficient DOM operation with only one operation per action. React and Vue, although still considered performant, showed slower rendering times of 170 ms and 140 ms, respectively, along with larger bundle sizes and higher memory usage. Svelte’s superior performance is largely due to its compiler-based architecture, which transforms components into highly optimized vanilla JavaScript without relying on a virtual DOM or heavy runtime. This study provides valuable insights for developers and decision-makers in selecting the most technically efficient frontend framework for high-performance web applications.
Analysis of Direct Scoring and Similarity-Based Scoring Approaches in Automatic Short Answer Scoring (ASAS) Wicaksono, Bayu; Rasim, Rasim; Wihardi, Yaya
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6275

Abstract

In the era of digital education, the need for automated scoring systems for short text answers has been steadily increasing. Automatic Short Answer Scoring (ASAS) aims to automate this assessment process with efficient and consistent approaches. Two commonly used approaches in ASAS are direct scoring and similarity-based scoring. Although these two approaches have been widely used, previous research has mostly focused on metrics like RMSE and Pearson Correlation to assess model performance. This study aims to provide a more in-depth analysis by comparing both approaches in two evaluation scenarios, specific-prompt and cross-prompt, by evaluating the accuracy and stability of the models. The dataset used in this study is the Rahutomo dataset. The results of the analysis show that direct scoring outperforms similarity-based scoring in terms of lower RMSE, higher Pearson Correlation, and fewer outliers. In the specific-prompt scenario, an RMSE of 0.0817 and a Pearson Correlation of 0.9504 were obtained, while in the cross-prompt scenario, the RMSE was 0.0917 and the Pearson Correlation was 0.9286. This study provides a more comprehensive insight into model performance by not only relying on evaluation metrics but also examining the distribution of residuals and outliers, which offers a more complete picture of model stability. Based on these findings, direct scoring is recommended for implementation in ASAS systems and for future research that can extend the analysis to other datasets or languages.
Security Analysis And Data Recovery On Large-Scale Computer Networks Putra, Fauzan Prasetyo Eka; Syirofi, Syirofi; Wahid, Durrahman; Syam, Abd. Mu’iz
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6276

Abstract

Large computer networks are now needed to help institutions, businesses, and governments do their work in today's complex and connected digital world. At the same time, the growth of very large and important data sets brings new problems for keeping data safe and quickly getting it back when needed. This research aims to carefully study security plans and data recovery methods that can be used well with large computer networks. This research looks at scientific papers, information technology articles, and related research reports from the last ten years. The main things to do are to use firewalls in layers, use encryption methods like AES and RSA, and use systems to find and block malware by looking at its signatures and actions. We also need to make the physical security of network devices stronger, automatically manage updates, and set up regular data backups along with a plan for recovering from disasters (DRP). The study's results show that a complete security and recovery system that combines different parts can greatly improve how well the system can handle different threats, whether they are technical or not. Working together to prevent cyberattacks and being ready to recover data is very important for keeping information technology services running smoothly. This research gives key suggestions on how to build a network that is not only secure and works well but can also quickly recover if there is an emergency or system problem.
Evaluating User Interfaces in E-Learning Satisfaction Using EUCS Method Angrayni, Cindy; Panjaitan, Erwin Setiawan
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6302

Abstract

This study investigates how User Interface elements affect student satisfaction with E?Learning platforms among junior high school, senior high school, and vocational high school students in Medan by adopting the End User Computing Satisfaction model augmented with a dedicated User Interface quality construct. It examines Content, Accuracy, Format, Ease of Use, Timeliness, and overall Interface Perception as predictors of End User Computing Satisfaction. Data were gathered via questionnaires administered to students actively using E?Learning applications across these three school levels in Medan and Structural Equation Modeling was employed to explore the complex interrelationships among these constructs. Results reveal that Accuracy, Format, Ease of Use, and Timeliness each exert positive and significant influences on perceived Interface quality, with Ease of Use contributing most strongly, while Content presentation alone does not significantly affect perceptions. Furthermore, perceived Interface quality demonstrates a very strong and significant impact on End User Computing Satisfaction. These findings indicate that in the context of Medan’s junior high school, senior high school, and vocational high school digital learning environments, intuitive usability, clear visual formatting, information accuracy, and prompt delivery are critical drivers of student satisfaction. This study underscores the need for E?Learning developers and educational policymakers to prioritize intuitive, reliable, and responsive interface designs and recommends that future research broaden sample coverage to additional regions, compare multiple E?Learning platforms, and adopt longitudinal designs to capture how Interface element effects on student satisfaction evolve over time.
Facial Expression Recognition of Students in Classroom Using Hybrid MobileNetV3-Vision Transformer with Token Downsampling Khaairi, Mochamad; Rasim, Rasim; Wihardi, Yaya
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6323

Abstract

In large classroom environments, teachers often struggle to monitor each student’s facial expression throughout the learning process. Yet, facial expressions are important indicators of students’ emotional states and engagement, which, when detected in real time, can support a more adaptive learning experience. Most previous research on Facial Expression Recognition (FER) has relied on Convolutional Neural Networks (CNN), which tend to be limited in capturing global relationships between facial features. Additionally, many studies focus on model accuracy without evaluating their practical effectiveness in real classroom settings. This study aims to develop a facial expression recognition model that is both accurate and efficient for use in classroom contexts. A hybrid Vision Transformer (ViT) architecture is proposed, which combines MobileNetV3 for local feature extraction and a Vision Transformer for global context modeling. To reduce the number of tokens and computational cost, a Token Downsampling method is introduced within the transformer blocks. The model is trained using the FER2013 dataset and achieves a test accuracy of 71.24%, surpassing the baseline pretrained ViT model, which reached only 70.10%. Additionally, the Token Downsampling method improves inference speed. Furthermore, the model is tested on a custom dataset collected from students in a real classroom setting to evaluate its performance in practical implementation. Although the performance on the classroom dataset is not yet optimal, the results on FER2013 demonstrate the potential of this approach for further development toward real-time and accurate facial expression recognition in educational environments.
Sentiment Analysis to Detect Public Anxiety About the HMPV Virus On Social Media Purba, Handryansyah; Panjaitan, Erwin Setiawan
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6329

Abstract

This study aims to analyze the level of public anxiety towards the Human Metapneumovirus (HMPV) virus, through a sentiment analysis approach carried out on social media. In this study, the Word2Vec model is used as the main method for feature extraction, which functions to represent words based on relevant semantic contexts. This approach allows for a deeper understanding of the meaning of words in people's comments and posts. In addition, two main classification algorithms, namely Support Vector Machine (SVM) and Bidirectional Long Short-Term Memory (Bi-LSTM), are evaluated to determine their effectiveness in detecting and classifying primary sentiments, whether positive, negative, or neutral. The data collected came from Twitter using a special crawling method, resulting in 5,000 tweets that have been manually labeled according to their categories. The results showed that the Word2Vec model with a 200-dimensional vector was able to capture relevant and deep semantic meanings towards social and health contexts. For the classification algorithm, SVM obtained an accuracy of 82.67%, although it had difficulty identifying neutral sentiments. In contrast, Bi-LSTM performed better, with an accuracy of 89.72%, and was able to recognize emotional patterns that were not explicitly visible in the data. These findings confirm that the combination of Word2Vec and Bi-LSTM is the most effective approach to detecting public anxiety about health issues. This study also provides important insights into the dynamics of public sentiment on social media and opens up opportunities for the development of more adaptive sentiment analysis models in the future.
Website-Based Kakaomu Application Development Haedar, Arjuna; Thamrin, Andi Nurlinda; Siswanto, Rahmat
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6332

Abstract

The development of digital technology, especially in artificial intelligence (AI), has brought about major changes in various sectors of life, including agriculture. AI allows agricultural activities to be carried out more efficiently, accurately, and based on data. One of the agricultural commodities affected by this progress is cocoa. As one of the largest cocoa producers in the world, Indonesia has a great opportunity to develop this sector. However, the challenges in the field are still quite complex, especially in areas such as Pongo Village and North Luwu Regency. Cocoa farmers in this area face various problems, such as Helopeltis spp. pest attacks, declining plantation productivity, lack of market information, and lack of a comprehensive cocoa product tracking system. These problems result in suboptimal harvests and difficulty competing in the global market. To answer this challenge, the development of technology-based solutions is very important. One of the proposed innovations is the Kakaomu web application, which utilizes artificial intelligence to assist farmers in various aspects of agriculture. The main features of this application include pest identification and control, monitoring current market prices, recording production results, and a digital traceability system from the plantation to the end consumer. With the adoption of this technology, cocoa productivity, efficiency, and quality can increase significantly. In addition, this can also strengthen Indonesia's cocoa position in the world export market and encourage more modern and sustainable agriculture.
Design of Letter Management System at Kanna Utara Village Office Based on Website Annas, Syahratul; B, Isdayani; Siswanto, Rahmat
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6333

Abstract

This study aims to design a correspondence management system in the Kanna Utara village office based on a website that previously used manual methods through bookkeeping and Microsoft Word/Excel applications; these methods have various weaknesses, such as easy archive damage, limitations in searching for letters, and potential data loss. The system, which was designed using the Laravel framework with an agile development approach, aims to improve efficiency, security, and ease of access for both village officials and the community. This qualitative research approach uses observation and interviews to identify user needs, such as digital management of incoming and outgoing letters, online submission by the community, and tracking of letter status in real time. This system has separate features between Kanna Utara village office employees and the community to ensure access authority according to their respective roles. Kanna Utara village office employees can monitor and manage letters through the admin dashboard, and the community can submit letter requests and view letter status on the public dashboard. The simple interface makes it easy for village officials with technical limitations to operate the system. Although the system is running well, development, such as digital signatures and automatic notifications via short messages, is still needed. This study concludes that digitalizing the correspondence system at the village level is possible and can be used as a model for other villages with similar conditions.

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