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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 Articles sent to the Brilliance may not be published elsewhere.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 594 Documents
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AI Social Interaction, Experience, and AI Literacy in Identifying AI Content among Jabodetabek Users Dani Surya Wijaya; So Yohanes Jimmy; Trihadi Pudiawan Erhan
Brilliance: Research of Artificial Intelligence Vol. 6 No. 3 (2026): Brilliance: Research of Artificial Intelligence, Article Research August 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i3.9033

Abstract

Background: Generative AI has intensified the circulation of synthetic visual content and increased the need for users to identify AI-generated content. Prior studies have examined deepfake detection, AI literacy, AI experience, and social interaction separately, but empirical evidence on how these factors jointly shape identification ability remains limited. Objective: This study investigates the effects of AI Social Interaction and AI Experience on AI Content Identification Ability, with AI Literacy positioned as a mediating mechanism. Methods: A quantitative survey was conducted with 270 social media users in Jabodetabek. AI Social Interaction, AI Experience, and AI Literacy were measured using a five-point Likert questionnaire, while identification ability was measured through a 30-item objective performance test consisting of AI-generated and authentic images and videos. Data were analyzed using partial least squares structural equation modeling with SmartPLS 4.0. Results: AI Experience had a positive effect on AI Literacy. AI Literacy and AI Social Interaction had significant direct effects on AI Content Identification Ability. AI Experience did not directly affect identification ability, but its indirect effect through AI Literacy was significant, indicating full mediation. Conclusion: AI use experience does not automatically improve the ability to identify synthetic content. Experience must be converted into evaluative and ethical AI Literacy, while social interaction may support direct pattern recognition through digital exposure.
Topic Classification on Twitter Using a Multi-View Graph Neural Network (GNN) Model Dhea Sila Mukti; Rizal Tjut Adek; Cut Agusniar
Brilliance: Research of Artificial Intelligence Vol. 6 No. 3 (2026): Brilliance: Research of Artificial Intelligence, Article Research August 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i3.9061

Abstract

Political discussions on social media have become an important source of information for understanding public opinion and information dissemination. However, most existing political topic classification methods rely primarily on textual features and tend to overlook structural and temporal relationships between users. In this study, we propose a Multi-View Graph Attention Network (MV-GAT) that improves political topic classification by integrating three complementary graph representations: a semantic content graph, a user interaction graph, and a temporal propagation graph. We collected a dataset containing 15,131 Indonesian tweets from Twitter(X), of which 1,677 tweets were manually labeled as political or apolitical, and the remaining tweets were kept as unlabeled nodes to maintain the graph structure. Each graph view was independently constructed and aligned using tweet_id before being processed by the proposed MV-GAT model. The model was trained using weighted cross-entropy loss with an attention-based fusion mechanism to automatically learn the contribution of each graph view. Experimental results showed that the proposed method achieved an accuracy of 84.23%, a macro F1 score of 83.04%, and an F1 score of 78.54% in political topic classification. Attention analysis revealed that the semantic content graph contributed most significantly to the classification process, while the interaction graph and time graph provided complementary structural information. Furthermore, post-classification graph analysis revealed relationship patterns among users and the propagation of political information within the Twitter network. These results demonstrate that integrating multiple graph views improves both the classification performance and interpretability of political topic analysis on social media.
Performance Evaluation of OSPF and EIGRP Routing Protocols for Scalable Distributed Network Environments Akbar Maulana Mubarak; Atep Aulia Rahman
Brilliance: Research of Artificial Intelligence Vol. 6 No. 3 (2026): Brilliance: Research of Artificial Intelligence, Article Research August 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i3.9065

Abstract

Distributed computer networks require routing protocols capable of maintaining efficient communication as network size and complexity continue to increase. Selecting an appropriate routing protocol is essential to ensure reliable data transmission, efficient routing performance, and network scalability in distributed computing environments. This study compares the performance of two dynamic routing protocols, namely Open Shortest Path First (OSPF) and Enhanced Interior Gateway Routing Protocol (EIGRP), using equivalent distributed network topologies to ensure consistent testing conditions and objective performance evaluation. The experiments were conducted using Graphical Network Simulator 3 (GNS3) for the OSPF implementation on MikroTik RouterOS and Cisco Packet Tracer for the EIGRP implementation on Cisco devices. Network performance was evaluated based on four parameters: latency, packet loss, convergence time, and scalability. The experimental evaluation demonstrated that OSPF achieved lower average latency and established a greater number of neighbor relationships, indicating better scalability and efficient route management within the simulated distributed network environment. In comparison, EIGRP produced lower packet loss while maintaining reliable routing communication throughout the experiments, reflecting its capability to provide stable packet delivery. The convergence performance of both routing protocols was evaluated under the implemented testing scenario, where both protocols demonstrated comparable routing recovery behavior. These findings indicate that each routing protocol offers different advantages depending on network size, topology characteristics, and operational requirements. Therefore, selecting an appropriate routing protocol should consider the expected scalability, communication reliability, and overall performance requirements of the target distributed network environment.
Sentiment Analysis of TikTok Netizens on Oil Palm Issues in Papua Using KNN Annisa Karima; Naufal Abdulillah; Aril Maulana; Satria Harry Menov; T. Sukma Achriadi Sukiman
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.8073

Abstract

Oil palm plantation in Papua has become a controversial issue that has generated diverse responses from the public, particularly regarding concerns over environmental impacts and ecosystem sustainability. These differing perspectives create the need to comprehensively and objectively understand public perceptions. This study aims to analyze the sentiment of Indonesian netizens toward the policy of oil palm plantation in Papua using a machine learning–based sentiment analysis approach. The research data were collected from user comments on the TikTok platform, which were subsequently processed through preprocessing stages, translation into English, and automatic labeling using TextBlob. The labeled data were then represented using Term Frequency–Inverse Document Frequency (TF-IDF) weighting and classified using the K-Nearest Neighbors (KNN) algorithm. The classification results using the K-Nearest Neighbors (KNN) algorithm indicate that out of a total of 220 data samples, 31 data (14%) were classified as positive sentiment and 189 data (86%) as negative sentiment. The classification process using the K-Nearest Neighbors (KNN) algorithm with the optimal K value of 5 achieved an accuracy of 77.27%, with a precision of 74.90%, recall of 66.00%, and an F1-score of 67.65%. The recall value for the positive class is relatively low, at 38.46%, indicating that the model still faces challenges in correctly identifying all positive data. This limitation is attributed to the imbalance in data distribution and the complexity of language used in social media comments. Nevertheless, the overall classification results suggest that the majority of netizens tend to oppose oil palm plantation in Papua, mainly due to concerns about environmental impacts and ecosystem degradation.
Analysis Of Population Data Grouping Using K-Means For Efficiency Data Centralization And Backup Putri Alda; Rika Nofitri; Edi Kurniawan
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.8264

Abstract

Population data management at the village level plays an important role in supporting administrative services, development planning, and data-driven decision-making. However, in many village offices, including the Bandar Sono Village Office, population data is still managed manually and stored in fragmented formats, resulting in inefficiencies in data retrieval, duplication risks, and difficulties in performing structured data backup. This condition indicates the need for a more systematic approach to organizing and analyzing population data. This study aims to analyze and implement the K-Means clustering algorithm to group population data in order to improve the efficiency of data centralization and backup processes. The research method used is quantitative, involving data collection, preprocessing, and clustering analysis using the K-Means algorithm based on attributes such as the number of male residents, female residents, and total population. The results show that the K-Means method successfully groups population data into three clusters, namely small, medium, and large population categories. These clustering results provide more structured and meaningful information, facilitating easier data analysis and improving the effectiveness of data management. In conclusion, the implementation of K-Means clustering contributes to enhancing the efficiency, organization, and reliability of population data management systems, thereby supporting better administrative services and decision-making at the village level.
Optimization of Telkomsel 4G LTE Network in Taman Panorama Baru Area Afrizal Yuhanef; Deri Latika Herda; Herry Setiawan; Dhea Veriska
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.8303

Abstract

The Bukittinggi City New Panorama Tourism Park is one of the most visited destinations, requiring optimal cellular network quality to support communication and data services. This study aims to analyze the performance of Telkomsel’s 4G LTE network in the area, plan necessary improvements, and optimize network performance using Atoll software. The initial data were collected through a drive test by measuring key parameters such as Reference Signal Received Power (RSRP), Signal to Interference plus Noise Ratio (SINR), and throughput. Field measurements revealed that several areas still exhibited RSRP values ? -110 dBm, low SINR, and existing bad spots, indicating that the service quality had not yet reached optimal performance. Network optimization was carried out by adjusting antenna parameters including azimuth, tilt, and transmit power using the Automatic Cell Planning (ACP) method. The simulation results in Atoll after optimization showed improved RSRP coverage, a significant increase in SINR, and higher predicted throughput compared to the initial condition. However, discrepancies were found between simulation and field results, particularly in throughput, due to propagation model limitations, physical obstructions, and environmental variations. In conclusion, the implemented optimization successfully enhanced the overall network performance, although further evaluation is required to better align the simulation outcomes with real field conditions.
Web-Based Customer Relationship Management (CRM) System At Enc Audio to Improve Customer Satisfaction Khairun Nisa; Dewi Maharani; Santoso Santoso
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.8316

Abstract

This study aims to design and develop a web-based Customer Relationship Management (CRM) information system at ENC AUDIO, a business engaged in musical instrument and sound system rental services, in order to improve customer satisfaction and service quality. The main problem faced is that customer data, transaction records, and rental schedules are still managed manually, which often leads to data recording errors, scheduling conflicts, difficulties in tracking customer history, and inefficiencies in service processes. The research method used is the Operational CRM approach, with data collection techniques including observation, interviews, and documentation. The system is developed using the CodeIgniter framework and MySQL database, while system testing is conducted using the black box testing method to ensure that all system functions operate properly according to user requirements. The results of this study indicate that the developed CRM system is able to automate customer service processes, manage booking schedules in real time, and store customer transaction history in a structured and integrated manner. In addition, features such as online booking, automated scheduling, and live chat significantly enhance communication between customers and administrators. Therefore, the implementation of this system improves operational efficiency, reduces errors, and strengthens customer relationships, ultimately contributing to increased customer satisfaction and business sustainability.
Implementation Of SCM Information System At Street Coffee For Optimization Of Distribution Stock Control Rizka Fadhila; Nofriadi Nofriadi; Febby Madonna Yuma
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.8320

Abstract

The development of information technology has significantly influenced business operations, particularly in supply chain management (SCM), which requires efficient, accurate, and integrated systems to support decision-making processes. However, many small and medium enterprises still rely on manual data processing, which can lead to inefficiencies and operational constraints. At Street Coffee, inventory management and raw material distribution are still handled manually, resulting in discrepancies between recorded and actual stock levels, delays in procurement processes, limited real-time monitoring, and difficulties in coordinating with suppliers. These issues negatively impact operational efficiency and service quality. This study aims to design and implement a web-based SCM information system to improve inventory control, enhance data accuracy, and optimize distribution processes. The research employs a qualitative approach, with data collection methods including observation, interviews, and documentation. System development is carried out using Unified Modeling Language (UML) for system design and database modeling to ensure structured data management. The results indicate that the implemented system successfully integrates inventory, supplier, and distribution data into a centralized platform. The system enables real-time stock monitoring, minimizes data entry errors, improves coordination between procurement and inventory processes, and supports faster and more accurate decision-making. Therefore, the implementation of the SCM system contributes significantly to improving operational efficiency, effectiveness, and overall business performance at Street Coffee.
Implementation Of A Perfume Recommendation System Using Ahp And Topsis At Ivan Parfume Winda Nurdiana Putri; Dewi Maharani; Abdul Karim Syahputra
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.8354

Abstract

The rapid growth of the refill perfume industry requires business owners to enhance service quality, particularly in assisting customers in selecting suitable fragrance products. At Ivan Parfume, the large variety of available scents often causes confusion among customers, while the current recommendation process remains manual and subjective. This study aims to develop a web-based Decision Support System (DSS) using a combination of the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods to provide objective and accurate perfume recommendations The AHP method is employed to determine the priority weights of decision criteria, including price, longevity, packaging design, volume, and scent, while the TOPSIS method is used to rank perfume alternatives based on their closeness to the ideal solution. The system processes ten perfume alternatives and generates a ranked list of recommendations based on multi-criteria evaluation. The results indicate that the system is capable of producing structured and consistent recommendations aligned with user preferences. Furthermore, the system demonstrates good performance in handling multiple criteria simultaneously and provides transparent calculation results that can be easily interpreted by users. The implementation of the AHP-TOPSIS model improves decision-making efficiency by reducing subjectivity and processing time compared to conventional methods. This study demonstrates that the proposed system can effectively support retail businesses in delivering data-driven recommendations and enhancing customer satisfaction.
Unveiling Digital Transparency: Internet-Based Financial Reporting Disclosure by Indonesian Local Governments Fifi Yusmita; Arif Kurniawan
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.8443

Abstract

Internet Financial Reporting Disclosure (IFRD) represents a critical instrument for enhancing transparency and public accountability in local government, as mandated by the Ministry of Home Affairs Regulation No. 188.52/1797/SJ of 2012 however, its implementation remains uneven across regions. This study examines the effect of Local Own Source Revenue, Local Expenditure, Audit Opinion, and Size of Local Government on IFRD in Indonesia. Employing a quantitative approach with panel data from 2020 to 2023, this study analyzes 126 observations selected through purposive sampling and applies panel data regression using EViews 12, following model selection and classical assumption tests to ensure robustness. The empirical results demonstrate that all independent variables have a positive and significant effect on IFRD, indicating that stronger fiscal capacity, higher expenditure allocation, favorable audit outcomes, and larger institutional scale are associated with greater financial disclosure. These findings are consistent with Signalling Theory, suggesting that well-performing local governments tend to disclose more information to reduce information asymmetry and signal accountability to the public. Furthermore, the results highlight that transparency is shaped by the combined influence of financial performance and institutional characteristics rather than a single determinant. These findings imply that strengthening financial management, improving audit quality, and enhancing institutional capacity are essential to optimize internet-based financial disclosure, reinforce public trust, and support the effective implementation of good governance in local governments.

Page 59 of 60 | Total Record : 594

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