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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 Articles sent to the Brilliance may not be published elsewhere.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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Database Vulnerability Analysis of North Aceh e-Kinerja Website Using SQL Injection Fidyatun Nisa; Muhammad Ikhwani; Nanda Sitti Nurfebruary; Siti Nayla Husna
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.v5i2.6780

Abstract

The rapid advancement of information technology has significantly increased the risk of cyber threats, particularly in web-based systems. One of the most common attack techniques used to exploit vulnerabilities in web applications is SQL injection, which can result in sensitive data leakage and system compromise. This study aims to evaluate the database security of the E-Kinerja website of North Aceh Regency against SQL injection attacks using a black-box penetration testing approach. The assessment is conducted based on the Information Systems Security Assessment Framework (ISSAF), which provides a structured and systematic methodology for comprehensive security evaluation. The testing process includes several stages, namely planning and preparation, information gathering, network mapping, vulnerability identification, and penetration testing, utilizing tools such as SQLMap and OWASP ZAP. The results indicate that the target website is not vulnerable to SQL injection attacks, as no exploitable parameters were identified during testing. This is largely due to the implementation of security mechanisms such as Web Application Firewall (WAF) and Intrusion Prevention System (IPS), which effectively detect and prevent unauthorized access attempts. This study highlights the importance of implementing layered security strategies and continuously updating security protocols to address emerging cyber threats. The findings contribute to improving database security awareness and provide practical recommendations for strengthening the resilience of information systems in the government sector.
Assessment of E-learning Activity During COVID-19 Pandemic using Data Science Technique Eka Angga Laksana; Viddi Mardiasyah; Sunjana Sunjana; Yosi Malatta Madsu; Iwa Ovyawan Herlistiono; Andry Septian Syahputra Tumaruk
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.v6i1.7979

Abstract

The emerging of COVID-19 pandemic has become a threat to humanity, many activities of higher education forced to use Learning Management Systempad. This sudden transition significantly changed traditional face-to-face learning into fully online or blended learning environments, requiring both lecturers and students to quickly adapt to digital platforms and new methods of interaction.It provides various tools such as online quizzes, discussion forums, assignment submissions, and learning resources that can be accessed anytime and anywhere. Through these features, lecturers are able to distribute materials, monitor student participation, and evaluate learning outcomes more efficiently.The log records include information such as login frequency, access to learning materials, participation in discussion forums, quiz attempts, and assignment submissions.By applying data mining, statistical analysis, and data visualization methods, complex and unstructured log data can be transformed into meaningful insights. These visual representations help management identify trends, monitor student engagement, evaluate learning effectiveness, and support strategic decision-making in improving the quality of education.Processing log large data was optimized by the use of Graphics Processing Unit (GPU) and python programming language to extract, transform and load data (ETL) then convert the information to specific chart. By analyzing the result, we found some information regarded to student total activities by date, day, hour and also heatmap chart which represent total student activities by hour and day. Finally, the whole series of the processes are proposed as the assessment of e-learning activity on higher education.
Web-Based System Design And Implementation For Optimizing Pharmaceutical Logistics Management Samdika Maulana Hafiz; Arridha Zikra Syah; Mustika Fitri Larasati
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.v6i1.8258

Abstract

Medication logistics management plays a crucial role in supporting healthcare services, particularly in ensuring the availability of medicines at community health centers. However, the Sei Kepayang Barat Community Health Center still faces several challenges due to the use of manual systems in recording, monitoring, and reporting pharmaceutical data. These limitations often result in data inconsistencies, delays in information processing, and difficulties in tracking drug stock in real time. This study aims to design and implement a web-based pharmaceutical logistics information system to improve the effectiveness and efficiency of drug management processes. The research employed a qualitative approach through observation, interviews, and literature review to identify system requirements and existing problems. The system was developed using PHP and MySQL, with system design modeled through Unified Modeling Language (UML), flowcharts, and Entity Relationship Diagrams (ERD). The results indicate that the implemented system is capable of integrating key processes, including drug inventory management, procurement, distribution, and reporting. Furthermore, the system enables real-time monitoring of stock levels, reduces human error in data entry, and accelerates report generation. Overall, the web-based system contributes to improving the accuracy, transparency, and efficiency of pharmaceutical logistics management at the health center, thereby supporting better decision-making and enhancing the quality of healthcare services.
Design Construction of an Automatic Liquid Soap Dispenser Based on an Infrared (IR) Sensor Riski Riski; Kemas Muhammad Wahyu Hidayat; Zulhipni Reno Saputra Elsi
Brilliance: Research of Artificial Intelligence Vol. 6 No. 2 (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.v6i2.8520

Abstract

This study aims to design and implement an automatic liquid soap dispenser based on an infrared (IR) sensor to Hand hygiene is an important factor in preventing the spread of disease, especially those caused by bacteria and viruses. The use of conventional liquid soap dispensers still requires physical contact, thus potentially causing cross-contamination. Therefore, this study aims to design and build an automatic liquid soap dispenser based on infrared (IR) sensors that can work without contact. The research method used is Research and Development (R&D), which includes the stages of needs analysis, system design, prototyping, and tool testing. This tool uses an infrared sensor to detect the presence of hands, Arduino Uno as the main controller, and a TDR Delay module to set the active duration of the liquid soap pump. When a hand is detected by the infrared sensor, the signal will be processed by the Arduino Uno to activate the delay module, then the DC pump will turn on for a predetermined duration so that liquid soap is dispensed automatically. After the set time is reached, the pump will automatically turn off and the system will return to standby mode. The test results show that the automatic liquid soap dispenser can work well and responsively in detecting hands and dispensing liquid soap consistently according to the set duration. This system is able to reduce direct physical contact, improve hygiene, and has a simple design, is energy efficient, and is easy to implement.
Refining Semantic Segmentation of Flood Images Using Edge Sharpening and CNN Naili Suri Intizhami; Eka Qadri Nuranti; Muhammad Anugrah; Sri Sukma Tahir
Brilliance: Research of Artificial Intelligence Vol. 6 No. 2 (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.v6i2.8536

Abstract

Post-disaster impact analysis is an important component in supporting mitigation planning, emergency response, and evidence-based decision-making after flood events. Visual data, such as flood images, can be used to identify affected areas and analyze environmental conditions through semantic segmentation. Semantic segmentation is a pixel-level classification process that assigns each pixel in an image to a specific object class. However, flood images collected from real-world conditions often have low visual quality, unclear object boundaries, and complex backgrounds, which may reduce the quality of segmentation results. This study proposes an edge-sharpening-based preprocessing approach combined with a Convolutional Neural Network (CNN) model to improve semantic segmentation performance on flood images. The proposed method applies unsharp masking to enhance edge and contour information before the images are processed by the CNN model. The experiments were conducted using flood and non-flood image datasets and compared with the ENet baseline and a modified ENet model. The evaluation was performed using visual comparison and quantitative metrics, including precision, recall, F1-score, accuracy, and mean Intersection over Union (mIoU). The results show that the proposed method achieved the best performance on the flood image dataset, with 98% precision, 98% recall, 98% F1-score, 97% accuracy, and 54% mIoU. These results outperform the comparative CNN models and indicate that edge sharpening can improve object boundary representation, particularly for flood images with blurred or low-quality visual characteristics.
Design and Development of an Integrated Attendance and Task Management Application Based on Android M. Putra Ramadhani; Syarifah Aini; Karnadi Karnadi
Brilliance: Research of Artificial Intelligence Vol. 6 No. 2 (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.v6i2.8543

Abstract

Employee attendance and task management are important elements in maintaining organizational productivity, accountability, and operational efficiency. Conventional attendance systems, such as manual recording and fingerprint devices, still face several limitations including fraud potential, inaccurate data recording, and lack of flexibility for field employees. This research aims to design and develop an integrated Android-based attendance and task management application by utilizing Global Positioning System (GPS), Location Based Service (LBS), visual attendance validation, and real-time task monitoring features. The system was developed using the Rapid Application Development (RAD) approach combined with Agile Development to accelerate system implementation through iterative processes and continuous user feedback. The application integrates attendance recording, GPS location validation, employee task assignment, progress monitoring, and deadline notifications into a single platform. System testing was conducted using the Black-box Testing method to evaluate application functionality and reliability. The results show that the developed system successfully performs core functions such as login, GPS-based attendance, task management, attendance reports, and task monitoring with a testing success rate of 94%. The system is also capable of reducing attendance manipulation through real-time location validation and activity monitoring mechanisms. This research contributes an integrated mobile solution that improves attendance accuracy, task transparency, work efficiency, and employee productivity in organizations with high workforce mobility.
IoT-Based Public Street Lighting Monitoring and Control System Using LoRa Communication Andi Ahmad Dahlan; Reza Fahlevi; Ummul Khair; A. Abd Jabbar
Brilliance: Research of Artificial Intelligence Vol. 6 No. 2 (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.v6i2.8579

Abstract

Public Street Lighting (PSL) systems are important infrastructures that support road safety, reduce criminal activity, and improve public comfort during nighttime conditions. However, conventional PSL systems still experience several limitations, including inefficient energy consumption, delayed maintenance processes, and the absence of real-time monitoring capabilities. This study aims to design and implement an Internet of Things (IoT)-based public street lighting monitoring and control system using LoRa communication technology and the MQTT protocol. The proposed system integrates ESP32 microcontrollers, LoRa E220-900T30D communication modules, Raspberry Pi gateway devices, MQTT brokers, and Node-RED dashboards. The system is also equipped with Light Dependent Resistor (LDR), Passive Infrared Receiver (PIR), and current sensors to support automatic lighting control, motion detection, and abnormal condition monitoring. The research method includes hardware and software design, system integration, and communication performance testing through local networks and internet connections. Experimental results show that the system successfully performs real-time monitoring and remote lighting control. The average transmission delay for node 1 was approximately 15 seconds, while node 2 experienced delays ranging from 21.89 seconds to 36.02 seconds depending on communication conditions and processor workload. The proposed system successfully improves operational monitoring efficiency, supports adaptive lighting control, and reduces energy consumption through dimmer-based lighting adjustment. The developed system can be implemented as an alternative smart city solution for intelligent and efficient public street lighting management.
Noise Source Identification in Industrial Machinery Using Acoustic Analysis Abdulqadir M. Alhadar; Osamah Ibrahim Ali Barka; Musbag Ahedery; Omer I. A. Hmellah; Nuri Salem Ali Abosetha
Brilliance: Research of Artificial Intelligence Vol. 6 No. 2 (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.v6i2.8690

Abstract

Industrial machinery can generate occupational noise that affects worker safety and machine reliability, yet general noise measurement does not show which component is responsible for the strongest sound. Objective: This study improves noise source identification in industrial machinery by combining acoustic signal analysis, frequency spectrum interpretation, and component level comparison for four representative machines. Methods: A lathe, a multi-spindle drilling machine, a cigarette manufacturing machine, and a pasta packaging machine were examined. Measurements were taken near motors, gearboxes, cutting zones, drilling heads, rollers, reels, and a packaging cutter using a calibrated sound level meter and a condenser microphone. Recorded signals were evaluated through waveform observation, dominant frequency estimation, and repeated component ranking. Results: The highest measured levels were produced by electric motor noise in the cigarette machine, lathe, and drilling machine, with values of 101.4, 101.6, and 103.5 decibels respectively. Gearboxes, rollers, reels, drilling heads, and the cutter also produced meaningful noise, but most were lower than the corresponding motors. The frequency spectrum showed distinctive tonal or cyclic components for each machine part. Conclusion: The method provides a practical route for locating dominant noise sources, prioritizing maintenance, and reducing occupational noise through targeted control of motors, transmissions, and cutting mechanisms.
Analysis of YOLO26 Model Performance with Transfer Learning in Detecting Coffee Bean Defects Adrian Chen; Eka Puji Widiyanto
Brilliance: Research of Artificial Intelligence Vol. 6 No. 2 (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.v6i2.8715

Abstract

Background: Indonesia is one of the world's largest coffee producers, yet post-roasting coffee bean defects remain a critical challenge that reduces product quality and market competitiveness. Manual sorting processes are inconsistent and prone to human visual limitations. Objective: This study aims to analyze the performance of the YOLO26 nano model with transfer learning for detecting and classifying post-roasting coffee bean defects, and to evaluate the effect of grid search-based hyperparameter tuning on model performance. Methods: A first-party dataset of 4,567 images covering five defect categories — insect damage, under roast, quaker, nugget, and shell — and one non-defect category was collected from three local Indonesian coffee roasting companies. After augmentation, the dataset expanded to 10,595 images with a 70:20:10 training-validation-testing split ratio. YOLO26, a deep learning-based object detection model released in January 2026, was applied using transfer learning and optimized through grid search hyperparameter tuning across optimizer, learning rate, epoch, classification loss, and weight decay configurations. Results: The model was evaluated using precision, recall, F1 score, mean Average Precision at IoU 50 (mAP50), and mean Average Precision at IoU 50-95 (mAP50-95) on the test dataset, with results demonstrating competitive multi-class detection performance across all defect categories. Conclusion: YOLO26 nano with transfer learning and hyperparameter tuning is a viable approach for automated post-roasting coffee bean defect detection, contributing to quality control advancements in the Indonesian coffee industry.
Macroeconomic Crisis Early Warning Model for Libya Using Machine Learning Omar Musbah Awedat Amir; Ahmed Jamah Ahmed Alnagrat
Brilliance: Research of Artificial Intelligence Vol. 6 No. 2 (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.v6i2.8826

Abstract

Macroeconomic instability in Libya is closely linked to oil-sector volatility, fiscal fragmentation, exchange-rate pressure, liquidity shortages, and interruptions in public financial management. These conditions make delayed policy responses costly and create a need for a transparent early warning model that can translate macroeconomic signals into timely risk alerts. This study develops a machine learning-based early warning model for predicting macroeconomic crisis risks in Libya using secondary macroeconomic indicators and a scenario-based validation design. The proposed framework integrates data cleaning, lag construction, volatility measurement, crisis-risk labelling, model comparison, and interpretable risk explanation. The model architecture compares conventional statistical classification with tree-based ensembles, support vector learning, and neural network approaches. The results show that nonlinear ensemble methods are conceptually more suitable for Libya because they can capture interactions among oil disruption, fiscal pressure, exchange-rate pressure, liquidity stress, and inflation acceleration. The proposed risk dashboard classifies macroeconomic conditions into low, moderate, and high-risk states and links each alert to the main contributing indicators. The discussion highlights that the model should not replace expert judgement, but it can strengthen evidence-based policy monitoring, improve institutional coordination, and provide earlier signals for fiscal, monetary, and reserve-management decisions. The study concludes that an interpretable early warning system can support Libya’s macroeconomic resilience if regularly updated with reliable official data and governed through transparent validation procedures.

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