cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Muhammad Khoiruddin Harahap
Contact Email
choir.harahap@yahoo.com
Phone
+6282251583783
Journal Mail Official
publikasi@itscience.org
Editorial Address
Medan
Location
Unknown,
Unknown
INDONESIA
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 Articles sent to the Brilliance may not be published elsewhere.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
 49   50   51   52   53 
Disagreement Analysis of Sentiment Predictions on Student Satisfaction Surveys Using Two IndoBERT Models Nashihin, Durrotun; Lisnani, Lisnani; Hanafi, Imam
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7093

Abstract

Understanding student satisfaction survey presents both opportunities and challenges in a higher education. While sentiment analysis offers an efficient means of interpreting large volumes of textual data, inconsistencies between models can affect the reliability of resulting insights. This study aims to compare two IndoBERT sentiment models by analyzing their disagreement patterns and deriving insights to enhance institutional understanding of student satisfaction. The methodology involves two pretrained models (IndoBERT base finetuned SMSA and IndoBERT lite finetuned SMSA GooglePlay) applied to 657 student survey responses without additional fine-tuning. Evaluation focuses specifically on disagreement cases between the two models, using precision, recall, F1-score, accuracy and weighted average to assess model consistency. The results indicate that IndoBERT base demonstrates stronger contextual reliability, achieving a weighted average F1-score of 0.60 compared to 0.24 for IndoBERT lite on disagreement cases. IndoBERT lite tends to overestimate positive sentiment, particularly for short or ambiguous text inputs, whereas IndoBERT base maintains a more balanced interpretation across sentiment categories. The result from IndoBERT base also shows that positive sentiment gives the highest percentage at 53.4%, followed by neural at 34.6% and negative at 12.0% respectively. The negative sentiment is most likely related to campus facilities. These findings highlight that the disagreement analysis is valuable for identifying model biases and can provide insights to support institutional improvement from student satisfaction survey. For future research, more robust models can be developed by fining-tuned directly on student survey data, along with developing user-friendly application to assist universities in extracting the student survey data.
Business Intelligence Roadmap for Tableau Dashboard Development in Higher Education Fatoni, Yuda; Utomo, Pradita Eko Prasetyo; Bintana, Rizqa Raaiqa
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7094

Abstract

Universities are increasingly required to make data-driven decisions, yet many are still hindered by static and non-interactive reports. This study addresses these challenges at the Jambi University with the aim of designing and developing a series of interactive dashboards using Tableau, applying an adaptive framework based on the Business Intelligence Roadmap. The research methodology includes three main stages: pre-development, development, and post-development. The technical process involves Extract, Transform, Load (ETL) of data from different datasets into a MySQL database that serves as a centralized data source before visualization. The main results of this study are seven functional dashboard prototypes that were successfully developed, covering data analysis of lecturers, graduates, and other strategic areas. This dashboard is capable of presenting key insights, such as the lecturer-to-student ratio and lecturer qualification profiles (29.8% holding a PhD), in a visual and interactive manner. Furthermore, the prototype was successfully integrated into a web interface, demonstrating the technical feasibility of its implementation. This study concludes that the application of an adapted BI Roadmap is an effective approach for dashboard development in an academic environment. The results not only provide a decision-support tool for the Jambi University but also offer a methodological framework that can be replicated.
Sentiment Analysis of Indonesian National Team in 2024 AFF Using Naive Bayes and KNN Adrian, Rahmad; Aryani, Reni; Abidin, Zainil
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7111

Abstract

Social media platforms like Twitter (now X) serve as key channels for public opinion on major events, including sports tournaments such as the AFF Cup, where sentiments reflect nationalism, criticism, and support. Prior studies have highlighted varying accuracies in sentiment classification for Indonesian football contexts, prompting comparisons of algorithms like Naive Bayes and K-Nearest Neighbors (KNN). This research aims to analyze public sentiment directions towards the Indonesian National Team during the 2024 AFF Cup and compare the performance of Naive Bayes and KNN algorithms. Data comprised 1,918 tweets collected from December 8, 2024, to January 8, 2025, reduced to 1,598 unique entries after preprocessing (cleaning, case folding, tokenizing, filtering, stemming). Sentiments were labeled as positive, negative, or neutral by linguistic experts. TF-IDF vectorized features, and SMOTE addressed class imbalance. Models were trained on 90:10 data splits and evaluated using accuracy, precision, recall, and F1-score, with visualizations including frequency diagrams and word clouds. Neutral sentiments dominated at 49.6%, followed by negative (27.3%) and positive (23.2%). Naive Bayes with SMOTE achieved 79.38% accuracy, outperforming KNN (50-53%). Word clouds revealed supportive terms in positives ("garuda", "semangat"), critical in negatives ("kalah", "pecat"), and factual in neutrals ("indonesia", "piala"). Naive Bayes proves more effective for this dataset, offering insights for team management. Future work should explore advanced models like SVM or BERT and expand data sources for broader generalization.
Role of AI in Business Management
Brilliance: Research of Artificial Intelligence Vol. 3 No. 1 (2023): Brilliance: Research of Artificial Intelligence, Article Research May 2023
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v3i1.2191

Abstract

The article discusses the increasing applications of AI in business management. In sales and marketing, AI-powered tools can help businesses understand customer needs, create personalized marketing campaigns, and improve customer engagement. AI is also revolutionizing supply chain management, improving efficiency, and agility by analyzing real-time data. Customer service is also transforming through AI-powered chatbots that handle routine queries, allowing human agents to focus on more complex issues. In financial analysis, AI provides accurate and timely insights into financial performance, risk management, and investment opportunities. The article also highlights the benefits of AI, including increased efficiency and productivity, improved accuracy and precision, and better customer experience. It also addresses some challenges of AI in business management, such as data quality and availability, skills and expertise, cost, ethics and bias, and integration with existing systems. Finally, it discusses the future potential of AI in business management, such as predictive analytics, personalized marketing, chatbots, and process automation.
Integrating Local Linguistic Features into Rule-Based Chatbots: A Framework for Makassar Language Dialogue Systems Thabrani. R, Thabrani. R; Faizal, Faizal; Suryani, Suryani
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7103

Abstract

The advancement of digital technology has transformed the way people communicate and access information; however, it has also led to a decline in the use of regional languages, including the Makassar language. This phenomenon highlights the need for language preservation efforts through technology-based approaches. This research was conducted at the Public Relations Bureau of the Makassar City Government with the aim of designing a web-based dialogue application using the Makassar language as an interactive medium to promote local culture and tourism. The study employed a qualitative approach through observation and literature review methods, and implemented the System Development Life Cycle (SDLC) model with the waterfall approach. The application was developed using PHP and JavaScript programming languages and adopted a rule-based method to build a chatbot capable of interacting naturally in the Makassar language. System testing was carried out using the black-box method to ensure functionality and reliability in responding to user inquiries. The results showed that the chatbot performed effectively, providing relevant information about Makassar City’s culture and tourism, and increasing public appreciation for local language and culture. This study concludes that the integration of web-based technology with regional languages not only contributes to cultural preservation but also strengthens communication between the government and the community in the context of digital transformation.
Analysis Of Hybrid Relay Selection In SDR-Based Cooperative Systems Khair, Ummul; Lumasa R, Ihsan; Veronica, Vera; Lifwarda, Lifwarda; Rifka, Silfia
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7135

Abstract

The cooperative communication system, involving multiple nodes that assist each other in data transmission, has been studied. In this system, the source sends information not only directly to the destination but also through an intermediate relay node. The relay receives the signal, which is then forwarded to the destination, resulting in the reception of two copies of the signal. Combining these signals results in enhanced strength and reliability. This configuration addresses signal fading issues commonly encountered in wireless communication due to reflection, scattering, and interference. The Hybrid Relay Protocol (HRP), combining Decode-and-Forward (DF) and Amplify-and-Forward (AF) methods, was implemented to improve relay efficiency and adaptability. The relay’s protocol choice depends on instantaneous Signal-to-Noise Ratio (SNR); if the SNR exceeds a threshold, DF is applied by decoding and forwarding a clean signal, otherwise, AF is used by amplifying and forwarding the received signal without decoding. Relay selection is optimized based on channel gains using a harmonic mean metric. MATLAB simulations demonstrated that the HRS system, with relay distances ranging from 4 to 12 meters, achieves significantly improved Bit Error Rate (BER) performance, for instance, obtaining BER as low as 0.00012 at 4 meters and ?23.5 dBm transmit power. Additionally, maximal ratio combining was employed at the destination, enhancing signal reliability. The results confirm the effectiveness of the HRS system for real-time applications on Software-Defined Radio (SDR) platforms, with potential for improving wireless network robustness under diverse channel conditions.
Software to Predict Computer Component Failures using Multinomial Naïve Bayes Based On User Information Sasongko, Randie; Devella, Siska
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7194

Abstract

Based on data released by the International Data Corporation (IDC), the global market for personal computing devices experienced a year-over-year growth of 7.3% in the second quarter of 2024. This marks the second consecutive quarter of positive growth after nine quarters of decline. The trend suggests an increase in computer usage intensity, which is directly associated with a higher likelihood of hardware failures, particularly in components such as hard drives, RAM, and motherboards. This research aims to create a mobile-based application that can automatically classify types of computer component damage based on user-reported issues. The approach utilizes machine learning, specifically the Multinomial Naïve Bayes algorithm, combined with the Term Frequency–Inverse Document Frequency (TF-IDF) method for text feature extraction. The training data consists of categorized complaint texts according to the type of hardware problem. The resulting model was integrated into a mobile application to enable automated damage prediction. Experimental findings indicate that the proposed model performs effectively, achieving an accuracy rate of 78% in identifying computer damage categories. In summary, the developed application can help both technicians and general users diagnose potential hardware problems more accurately and efficiently. This not only speeds up the troubleshooting process but also reduces diagnostic errors and unnecessary part replacements. Moreover, the integration of Natural Language Processing (NLP) and machine learning enables the system to continuously improve its accuracy and adaptability as it learns from new data over time.
Interactive Visualization of Food Security Trends in North Aceh with a Business Intelligence Dashboard Rosnita, Lidya; Ikhwani, Muhammad; Aidilof, Hafizh Al Kautsar; Munauwar, Muhammad Muaz
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7190

Abstract

Food security in North Aceh Regency faces multifaceted challenges, including production fluctuations, price instability, and fragmented monitoring data across various institutions. These issues often hinder timely decision-making and the formulation of effective policies. Therefore, this study aims to develop a comprehensive Business Intelligence (BI) dashboard that can interactively visualize food security trends in North Aceh to support data-driven and evidence-based decision-making. The research methodology involves integrating data from multiple sources such as the Central Bureau of Statistics (BPS) and the Department of Agriculture using the ETL (Extract, Transform, Load) process to ensure consistency and accuracy. A data warehouse was then designed to store and manage the consolidated datasets efficiently, followed by the development of an interactive visual dashboard as the main analytical tool. The resulting dashboard is capable of visualizing six key parameters of food security through thematic maps, trend graphs, and comparative charts that allow users to observe temporal and spatial patterns. Advanced interactive features such as filtering, drill-down analysis, and cross-filtering provide users with the flexibility to independently explore data from different perspectives. The analysis demonstrates that the BI dashboard effectively integrates fragmented datasets, simplifies complex information, and enhances analytical capabilities for stakeholders. Overall, the findings indicate that implementing an interactive BI dashboard is a strategic and innovative solution to transform food security monitoring in North Aceh into a more proactive, integrated, and adaptive governance system, thereby strengthening regional resilience and policy responsiveness.
Clustering-Based Identification of Governance Risks in Campus Environments Sumiadji, Sumiadji; Nurhasan, Usman; Haris, Zainal Abdul
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7205

Abstract

The growing implementation of governance models in higher education institutions has raised new challenges in accountability and performance. Although risk management and analysis in governance has received some attention in the literature, there is a lack of studies focusing on the use data-driven clustering algorithms. The current research focuses on the development and assessment of a web-based risk management information system that incorporates clustering principles to identify and map non-academic governance risks. The study followed the CRISP-DM framework and analyzed 678 risks from the Internal Audit Unit (SPI) of Politeknik Negeri Malang. The system employs K-Means clustering analysis to classify the risks into tiers based on performance indicators, budget, and risk severity. The system is equipped with data upload, preprocessing, logging, and cluster visualization modules. A comparative analysis of K-Means, DBSCAN, and Hierarchical Clustering showed that K-Means yields the best cluster quality with a Silhouette Score of 0.48, in comparison to DBSCAN (-0.705) and Hierarchical clustering (-0.395) . The developed system generated five distinct clusters corresponding to risks of varying settlement priority viz. very high, high, medium, low, and very low. The functional and usability assessments of the system confirmed that it provides automated and actionable insights on risks in a user-centric manner. The study has demonstrated the clustering of governance risks in higher education using K-Means is feasible. The incorporation of predictive analytics and real-time data would best support the research in active risk avoidance.
IoT-Based Monitoring System To Support Village Food Security In The Smart Village Concept Nunsina, Nunsina; Fitri, Zahratul; Nazimah, Nazimah; Ulva, Ananda Faridhatul
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7229

Abstract

Food security is a critical issue that directly affects a nation’s social, economic, and political stability. The growing demand for staple foods such as rice, corn, and soybeans continues to exceed domestic production capacity, posing challenges to sustainable agricultural development. To overcome these issues, the Smart Village concept presents an innovative solution by integrating digital technology into rural agricultural systems. This study focuses on applying the Internet of Things (IoT) to enhance agricultural productivity and food security in Bireuen Regency. An IoT-based monitoring prototype was developed to regulate essential environmental parameters—pH, electrical conductivity (EC), nutrient solution temperature, and air humidity—through real-time sensor data collection and automated control. Experimental implementation revealed that the system effectively maintained optimal conditions: pH between 5.5–6.5, EC from 1.2–2.0 mS/cm, temperature between 20–28 °C, and humidity ranging from 65–80%. These controlled conditions created a stable growing environment that significantly improved crop performance. The results demonstrated measurable benefits: lettuce productivity increased by 18%, water-use efficiency improved by 27%, and crop failure rates decreased by 20%. Such improvements indicate that IoT technology not only stabilizes environmental variables but also enhances resource utilization and supports sustainable farming practices. Overall, the integration of IoT-based monitoring systems within the Smart Village framework represents a strategic approach to modern agriculture, promoting efficiency, sustainability, and rural independence in food production.

Page 51 of 55 | Total Record : 544

 49   50   51   52   53 

Filter by Year

2021 2026


Reset
Filter By Issues
All Issue Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026 Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025 Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025 Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024 Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024 Vol. 3 No. 2 (2023): Brilliance: Research of Artificial Intelligence, Article Research November 2023 Vol. 3 No. 1 (2023): Brilliance: Research of Artificial Intelligence, Article Research May 2023 Vol. 2 No. 3 (2022): Brilliance: Research of Artificial Intelligence, Article Research November 2022 Vol. 2 No. 2 (2022): Brilliance: Research of Artificial Intelligence, Article Research May 2022 Vol. 2 No. 1 (2022): Brilliance: Research of Artificial Intelligence, Article Research February 2022 Vol. 1 No. 2 (2021): Brilliance: Research of Artificial Intelligence, Article Research November 2021 Vol. 1 No. 1 (2021): Brilliance: Research of Artificial Intelligence, Article Research May 2021 More Issue