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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 Articles sent to the Brilliance may not be published elsewhere.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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Enhancing K-Means Clustering for Journal Articles using TF-IDF and LDA Feature Extraction Surianto, Dewi Fatmarani; Surianto, Dewi Fatmawati
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.5547

Abstract

Clustering is a fundamental technique in data analysis, particularly in unsupervised learning, to group data with similar characteristics. However, the effectiveness of the K-Means algorithm in text clustering heavily depends on proper feature extraction. This study proposes an enhanced feature extraction approach by integrating Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) to improve clustering performance on journal article datasets. The dataset consists of 427 journal article abstracts collected from Google Scholar. The preprocessing steps include tokenization, stopword removal, and TF-IDF vectorization, followed by topic extraction using LDA, which serves as input features for the K-Means clustering algorithm. The optimal number of clusters is determined using the Silhouette Score, with the best result obtained at k=9, achieving a score of 0.6806. The practical implications of this study include improved accuracy in academic document clustering, with applications in journal recommendation systems, digital library indexing, and research trend analysis. The results demonstrate that the combination of TF-IDF and LDA produces more informative text representations, significantly enhancing clustering quality. This study contributes to text mining and data science by proposing a systematic preprocessing framework for document clustering. Future research could explore its application to full-text articles, hierarchical clustering, or deep learning-based models to further improve clustering performance.
Building a fish disease detection application based on Smart Fishery with Image Processing and Deep Learning Aryanti, Rista; Wibowo, Ari Purno Wahyu
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.v5i1.5719

Abstract

Indonesia has great potential in the fisheries sector, especially in catfish cultivation which is one of the main commodities. Catfish has advantages in fast growth, high durability to the environment, and high market demand. However, the main challenge in catfish farming is disease attacks that can lead to mass deaths and economic losses for farmers. This disease often arises due to poor water quality and lack of knowledge of farmers regarding its identification and handling. Deep Learning-based artificial intelligence technology with the Convolutional Neural Network (CNN) method offers automatic fish disease detection solutions through digital image analysis. The CNN model is able to recognize patterns and classify images with high accuracy, so it can be a quick and efficient diagnostic tool. This study developed a Smart Fishery-based fish disease identification system with Image Processing techniques to improve image quality before being analyzed using CNN. The implementation of this method is expected to increase the efficiency of detecting catfish diseases and provide appropriate treatment recommendations. The study results indicate that the developed system effectively achieves high accuracy in classifying fish diseases. Moreover, the system includes additional features, such as virtual fish growth measurement and texture analysis utilizing the Gray Level Co-occurrence Matrix (GLCM). This technology is anticipated to assist fish farmers in enhancing productivity and minimizing the risk of fish mortality due to diseases.
Prediction of Crime Cases in 2025 in India Using the Fuzzy Time Series Chen Model Method karima, Annisa; Zulfia, Anni; Sukiman, T. Sukma Achriadi; Ulya, Athiyatul
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.v5i1.5745

Abstract

India's natural beauty and culture, which attract the attention of international tourists, are less able to increase tourist visits due to high crime cases. Tourists' fear of visiting the country has a direct impact on decreasing economic turnover, so the local economy has become very low. Predictions of criminal cases aim to provide an overview of cases that will occur in the next period, therefore the government can take appropriate policies to reduce crime cases. These predictions enable policymakers to plan strategic and data-based preventive measures. The method used is the Fuzzy Time Series Model Chen, because this method can overcome data uncertainty, and offers simplicity and ease in application. Valid and credible criminal statistics data in India is obtained from the site www.kaggle.com. A trusted platform that provides various quality datasets. This data will be used as a basis for the analysis and prediction of criminal cases in India. The results of this research show that in the range of 60 months from January 2020 to December 2024 using the Fuzzy Time Series Chen Model method to predict the number of criminal cases in India produced predictions in January 2025 with cases of 188.36 cases with a MAPE error ratio of 9.08% which is included in outstanding forecasting category.
Making Learning Module Using Google Spaces for Kindergarten Teachers in Pangandaran Based on LMS (Learning Management System) Wahyu Wibowo, Ari Purno; Laksana, Eka Angga; Sukenda, Sukenda; Yustim, Benny
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.v5i1.5770

Abstract

Learning process Children of kindergarten age tend to get bored easily and must use a different approach, because at an early age children will get tired easily if taught something tire Computers with attractive visual displays, animations, and sounds can make the learning process feel like playing, so that they are more enthusiastic and motivated. to overcome this, it is necessary to create a more innovative multimedia-based teaching system on the material taught must have parameters that are visual, audio and interactive material, so that the material delivered can be easily measured according to learning outcomes, in this study the author tried to use the help of open source applications based on Google Spacess and LMS (Learning Management System) which can be used by kindergarten teachers in the Pangandaran area, by using this Google Spaces assistance, it turns out that teachers can create the same and more interactive teaching material, in the end with the help of digital media, the material can be adjusted to the individual abilities of each child's speed and can be evaluated directly because for example children who have mastered the material can proceed to the next stage, while on the contrary students who are left behind in learning can repeat more relaxedly repeating the learning, then with the help of teachers and the student learning process can be done independently and not limited by time, because the LMS module can be accessed at any time by students. From the results of the training held for 65 kindergarten teachers, 85% agreed that this LMS application was very helpful in teaching and easy to configure and re-develop.
Analysis of Public Sentiment Text Clustering on Tax Increases using Orange Data Mining on Twitter Maulana, Ibnu Azhar; Wibowo, Ari Purno Wahyu
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.v5i1.5787

Abstract

Taxes play an important role in the life of a nation and state, particularly in the implementation of national development. Recently, Indonesia issued a new policy to increase VAT to 12%. This policy has sparked a range of both negative and positive opinions from the public. As a result, various reactions and sentiments have been expressed by citizens regarding the policy. To analyze these public sentiments, text mining was carried out using the Orange Data Mining application, utilizing data from the Twitter platform to observe and evaluate Indonesian citizens' reactions. A total of 100 tweets were collected using relevant keywords to find content related to the policy. The results were then categorized into several sentiment groups based on the similarity of their content. After the text classification, the data was stored in a table showing the number of positive, negative, and neutral sentiments. This data was later visualized in a graph, which revealed that the most common reaction was disappointment, followed by confusion, enthusiasm, and lastly, anger. The results of this study indicate that many Indonesian citizens are disappointed with the VAT increase policy. Many believe that the government's use of tax funds has not been satisfactory. Therefore, the government is urged to improve its programs so that citizens can feel the benefits of the taxes they pay.
Analysis of User Satisfaction Level on PT. Semen Baturaja's Website Using the User Experience Questionnaire (UEQ) Method Hidayat, Muhammad; Yudiastuti, Helda; Yulianingsih, Evi; Oktarina, Tri
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.v5i1.5828

Abstract

This study aims to analyze user satisfaction with the website of PT Semen Baturaja by employing the User Experience Questionnaire (UEQ) method. As a digital representation of the company, the website plays a significant role in disseminating information, promoting products, and strengthening the overall corporate image. In today’s digital era, providing an optimal user experience is crucial to ensure that a website can effectively fulfill user expectations and needs. The UEQ method serves as a comprehensive tool for assessing user experience through six essential dimensions: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. Data were gathered by distributing structured questionnaires to a group of users who were randomly selected from visitors to the company’s website. The analysis revealed that the website excels in efficiency, indicating that users find it quick and responsive in performing tasks. Furthermore, the site received above-average scores in attractiveness, perspicuity, stimulation, and novelty, reflecting a visually appealing layout, ease of navigation, engaging elements, and creative features that enhance user interaction. However, the dependability dimension scored below average, highlighting concerns regarding the website’s consistency, reliability, and error management. These findings suggest that while the website performs well in several areas, enhancements in system stability and functional reliability are necessary. In conclusion, this study underscores the importance of continuously evaluating and refining website performance. The insights gained are expected to provide meaningful input for PT Semen Baturaja in its efforts to improve website quality and ensure a more satisfying and dependable user experience in the future.
Management Strategy for Optimizing Worker Wage Management through SDLC-Based Digital Transformation in Dodol Garut MSMEs Nurjanah, Yesti Siti; Sumarni, Reny; R, Zaki Ulfatiatul
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.v5i1.5833

Abstract

The wage management of daily workers in Micro, Small, and Medium Enterprises (MSMEs) often faces challenges due to manual record-keeping systems that are vulnerable to inaccuracies and delayed payments. This study aims to develop a digital wage management system using the System Development Life Cycle (SDLC) framework and to explore effective managerial strategies for optimizing payroll processes through digital transformation, with a case study at MSME PD. Intan Bestari. A qualitative descriptive approach was employed, incorporating data collection methods such as in-depth interviews, direct observation, and documentation analysis. The system development followed six structured SDLC phases: planning, analysis, design, implementation, testing, and maintenance. The research findings reveal that the developed system effectively automates various components of wage management, including employee attendance tracking, production output recording, wage calculation, and worker loan administration. The management strategies applied to support this system include an Operational Efficiency Strategy, which emphasizes process automation, standardization of administrative procedures, and centralized data handling to enhance accuracy and speed. Furthermore, a Digital Transformation Strategy was implemented by adopting SDLC-based technological solutions, ensuring system flexibility (agility), and enabling cross-functional integration between payroll, production, and procurement units. The implementation of this integrated digital system has significantly improved operational efficiency, reduced errors in data processing, accelerated wage disbursement, and enhanced transparency in wage administration at PD. Intan Bestari. This study offers valuable insights for MSMEs seeking to modernize their wage management practices, highlighting the critical role of strategic management in successful digital transformation.
Wireless Network Design at Pamekasan Regency Public Library Putra , Fauzan Prasetyo Eka; Irfan, Moh.; Aziz, Mohammad; Saputra, Rama Nurja
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.v5i1.5876

Abstract

The increasing demand for internet access in public service spaces, such as libraries, necessitates a reliable and efficient wireless network. This study aims to design a wireless network for the Pamekasan Regency Public Library to enhance user experience in accessing digital resources. The methodology includes a site survey, analysis of user needs, simulation of access point (AP) placement using Ekahau software, and evaluation based on signal strength, coverage, and user traffic. The study was conducted across three types of libraries: public, school, and campus libraries in Pamekasan. Each location was assessed for building structure, user density, and interference sources. The simulation results determined optimal AP placement, utilizing dual-band (2.4 GHz and 5 GHz) access points to achieve minimum signal levels of -67 dBm and a Signal-to-Noise Ratio (SNR) above 25 dB. The findings indicate that the proposed network design can provide stable, wide-ranging wireless access in all key service areas. This study concludes that effective network design, based on detailed site surveys and technical simulations, is crucial for supporting digital library services. The proposed design contributes to improving information access and is expected to serve as a reference for similar implementations in other public institutions.     
From Observation to Co-Creation: A Netnographic Review of AI-Driven Consumer Behavior Mundzir, Ahmad; Pratiwi, Kuniarti
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.v5i1.5885

Abstract

As artificial intelligence (AI) continues to transform the landscape of digital consumer experiences, traditional netnographic methods face significant disruption—both in terms of methodological execution and theoretical framing. This systematic review explores how the growing presence of AI, functioning not only as a technological tool but also as an agentic participant, reshapes the way netnography is practiced in marketing and consumer behavior research. Recognizing a widening gap in the literature, the study aims to synthesize current conceptual and methodological advancements at the intersection of ethnographic inquiry and intelligent systems. Employing the PRISMA 2020 protocol for systematic reviews, 43 peer-reviewed journal articles published between 2013 and 2024 were retrieved from Scopus and Web of Science databases. A thematic synthesis of these articles reveals five dominant themes: the methodological evolution of netnography within online consumer communities; the role of AI in predictive personalization; emerging patterns of consumer–AI value co-creation; new relational models of human–AI interaction; and ethical and epistemological challenges posed by AI-augmented ethnography. These themes collectively inform the development of a novel conceptual framework, AI-Netnography which positions both human and algorithmic agents as co-constructors of meaning, identity, and experience. By reimagining netnographic inquiry for AI-mediated environments, this review not only advances the field of qualitative marketing research but also proposes new pathways for ethically responsible and epistemologically inclusive digital consumer studies.
Comparative Analysis of Laravel and Symfony in PHP-Based Web Application Developmen Putra, Fauzan Prasetyo Eka; Kusuma, Okky Firmansyah; Mursidi, Moh.; Hamzah, Amir
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.v5i1.5892

Abstract

This research aims to compare two leading PHP frameworks, Laravel and Symfony, in the context of modern web application development. Using a Comparative Literature Study approach, this study analyses various relevant literature sources to evaluate the performance, ease of use, key features, security, as well as scalability and community support of each framework. Laravel is known for its simple and productive approach, making it popular among beginner to intermediate developers. Meanwhile, Symfony has a more complex and flexible architectural structure, which is often used in large-scale projects and enterprise systems. From the results of the study, it was found that Laravel is superior in terms of ease of use and the availability of extensive documentation, while Symfony shows more stable and flexible performance in large-scale applications. In terms of security, both frameworks provide protection features against common threats such as SQL Injection, CSRF, and XSS. Laravel has the upper hand in providing off-the-shelf solutions, while Symfony provides more advanced settings for experienced developers. Laravel's community support is also greater, making learning and development easier. This research provides an objective overview for developers in choosing a framework that suits the needs of the project, both from a technical and practical perspective.

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