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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 Articles sent to the Brilliance may not be published elsewhere.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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Evaluating Random Forest Algorithm: Detection of Palm Oil Leaf Disease Rahmanto, Oky; Julianto, Veri; Arrahimi, Ahmad Rusadi
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.v4i2.4798

Abstract

This research investigates the application of machine learning techniques for detecting diseases in oil palm leaves, utilizing a dataset of 1,119 images sourced from plantations in the Tanah Laut district. The dataset comprises 488 diseased and 631 healthy leaf samples, which were carefully cropped to isolate leaf areas and labeled with the assistance of domain experts. For feature extraction, both Lab and RGB color spaces were considered, alongside Haralick texture features, resulting in a total of eleven features per pixel. To reduce dimensionality and select relevant features, Principal Component Analysis (PCA) and Random Forest methods were applied. Support Vector Machine (SVM) was subsequently employed for the classification of leaf health status, and model performance was evaluated using accuracy, precision, recall, and F1 score metrics, all derived from a confusion matrix. The study finds that PCA and Random Forest significantly enhance model performance, improving the ability to distinguish between healthy and diseased leaves. These findings provide valuable insights for the development of automated disease detection systems in oil palm plantations, with potential applications in precision agriculture. Additionally, the results suggest pathways for further research into plant disease diagnostics, highlighting the role of advanced machine learning techniques in enhancing crop management and supporting sustainable agricultural practices.
Implementation of EVE-NG in Increasing the Effectiveness of Project-Based Learning in the Network Computer Engineering Technology Study Program Utomo, Hendrik Setyo; Sholeha, Eka Wahyu; Supriyanto, Arif; Rusmana, Deni; Majid, Al
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.v4i2.4817

Abstract

The increasing demand for practical skills in computer networks has made hands-on learning a crucial element in information technology education. This study focuses on evaluating the usability of EVE-NG, a network simulation platform, to optimize project-based learning for students in the Computer Engineering and Network Program (TRKJ) at Politeknik Negeri Tanah Laut. The students, in their third semester, were selected as respondents, as they were enrolled in the Computer Networks 3 course, which is relevant to the practical use of EVE-NG. A usability analysis was conducted using five key indicators: Learnability, Memorability, Efficiency, Errors, and Satisfaction, assessed through questionnaires. The findings revealed that EVE-NG performs adequately across all indicators, with average scores ranging from 2.82 to 3.04, indicating good usability overall. Learnability and Memorability were rated as "good," though some users reported challenges in adapting to certain interface elements. Efficiency was rated "fair," with some feedback highlighting the need to improve workflow speed. The Errors indicator showed relatively low user mistakes, suggesting that the system is generally intuitive, although some interface ambiguities remain. Satisfaction received the highest score, reflecting user contentment with the platform, though improvements in system speed and interface clarity were suggested. The results provide essential insights into the current usability of EVE-NG and offer recommendations for further enhancements. With targeted improvements, EVE-NG can offer a more efficient and user-friendly experience, better supporting practical, real-world network simulation tasks in educational settings.
Analysis of Public Opinion Sentiment towards the 2024 Presidential Election Based on Clustering Method with K-Means Algorithm Harahap, Mhd Anshor; Ikhsan, Muhammad
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.v4i2.4821

Abstract

The presence of social media, such as Twitter, Facebook and Instagram, provides a space for people to express their opinions freely and openly. Various sentiments, ranging from support to criticism of the candidates, work programs, and other political issues, have emerged along with the increasing public enthusiasm. Therefore, it is important to understand how public opinion is evolving and what is the main focus of public attention in the 2024 presidential election. The purpose of this research is to analyze the sentiment and views of the public about the presidential election using the Clustering approach and the K-Means method and to classify public opinion for various interests as well as optimizing social media information for the public interest. Based on the research conducted, the K-Means algorithm was successfully applied for sentiment analysis of public opinion on the 2024 presidential election, using tweet data taken through crawling Twitter as many as 220 tweets. From the dataset, 5 tweets were used for manual implementation of the K-Means algorithm calculation, through a series of pre-processing processes, including TF-IDF weighting. After the manual K-Means calculation, from 29 words generated from TF-IDF, the following clustering results were obtained: Cluster 0 (positive) contains 5 words, Cluster 1 (neutral) contains 18 words, and Cluster 2 (negative) contains 6 words. These results show that the K-Means algorithm can effectively cluster sentiment in public opinion data related to the 2024 presidential election based on patterns found in the words in the tweets.
Web-Based Coat an Kebaya Rental Information System in Palembang City Apriansyah, Apriansyah; Sobrina, Khoirunnisa Nabila; Haryanto, Dedi
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.v4i2.4825

Abstract

That study's objective is to develop and develop a web-based information system for renting suits and kebaya in Palembang City. This system is expected to facilitate the process of renting traditional clothing, especially suits and kebayas, for various events and occasions. The system will provide an online platform for customers to browse and select available Suits and Kebayas, make online reservations, and track their rental status. The system will also allow rental service providers to manage inventory, update availability, and accept online payments. The proposed system is expected to boost productivity and effectiveness of the renting procedure, increase customer satisfaction, and increase the competitiveness of rental service providers in Palembang City. The system will be developed using a web-based approach, and its functionality will be evaluated through usability testing and user acceptance testing. Because the information presented basically comes from input data (input), then before entering data you must check the data correctness of data so that the resulting information is appropriate. Prepare hardware and software support whose specifications can support this system well. Perform periodic data back-ups to anticipate undesirable events. This system cannot only be used for one place. It is hoped that this system can be used in all places or areas where the system is not yet computerized to save time and energy.
Automated Recognition of Batik Aceh Patterns Using Machine Learning Techniques Utaminingsih, Eka; Sahputra, Ilham
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.v4i2.4831

Abstract

This research focuses on the automatic recognition of Aceh batik patterns using machine learning techniques. Utilizing a Convolutional Neural Network (CNN) model based on EfficientNet, a dataset consisting of 1,200 Aceh batik images was processed through various stages, from data collection to model training and evaluation. The images are divided into three main classes: Bungong Jeumpa, Ceplok, and Kerawang. The data processing steps include normalization, resizing, and data augmentation to ensure better variation. The model was trained using 75% of the data as a training set and 25% as a testing set. The results indicate that the model performed excellently, achieving an accuracy rate of 98%. According to the classification report, the model achieved an average precision, recall, and F1-score of 0.98. The Kerawang category achieved the highest precision at 100%, while the Bungong Jeumpa and Ceplok categories had F1-scores of 0.98 and 0.97, respectively. These findings demonstrate the potential of machine learning methods in recognizing Aceh batik patterns with high accuracy, supporting the preservation of local culture through technology.
Leveraging Machine Learning for Sentiment Analysis in Hotel Applications: A Comparative Study of Support Vector Machine and Random Forest Algorithms Suryadi, Suryadi; Syahputra , Dedek; Astrianda, Nica; Syahputra, Rizki Agam; Suhendra, Rivansyah
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.v4i2.4877

Abstract

This research aims to conduct sentiment analysis on user reviews of hotel booking applications such as Trivago, Tiket, Booking, Traveloka, and Agoda, collected from the Google Play Store. The dataset used consists of 5,000 user reviews, with 80% of the data allocated for training and 20% for testing. Two algorithms applied in this study are Support Vector Machine (SVM) and Random Forest, with performance evaluation based on accuracy, precision, recall, and F1-score metrics. The test results show that the Random Forest algorithm delivers the best performance on the Trivago application with 94% accuracy, 94% precision, 100% recall, and a 97% F1-score. Random Forest proves to be more effective in handling diverse review data, while the Support Vector Machine (SVM) algorithm also produces good results in sentiment classification. This research contributes to the development of sentiment analysis based on user reviews, which can be utilized by app developers and hotel management to improve service quality and user experience.
Design and Development of a Web-Based Correspondence Management Information System at Politeknik Negeri Tanah Laut Rhomadhona, Herfia; Noor Hayatie, Marliza; Pebriana, Rina
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.v4i2.4878

Abstract

Correspondence at Politeknik Negeri Tanah Laut (Politala) is currently still done manually in the vertical delivery process between work units. This manual process results in inefficiency, especially in monitoring the status of documents such as Staff Review Documents. Staff Review is a document that must be made by the department to the director as a basis for requesting assignment letters, decrees, recommendation letters and the others. These documents often require repeated checking, causing delays in the workflow. In addition, paper-based correspondence archives increase the risk of losing documents and make it difficult to find them again. Therefore, the implementation of the Correspondence Management Information System (SiMantan) is needed to improve the efficiency of the correspondence administration process by providing electronic document management and real-time status monitoring. This information system is built using the waterfall development model and designed with the Unified Modeling Language (UML) in the form of Usecase Diagrams. While the programming language used is the PHP programming language with the Code Ignitor 3 framework and MySQL database. Functional testing of the system is carried out using the black box testing and user acceptance testing (UAT) method which shows that all features in the information system function properly according to the expected specifications.
Intelligent Infrastructure for Urban Transportation: The Role of Artificial Intelligence in Predictive Maintenance Alqasi, Mohammed Ali Younus; Alkelanie, Youssif Ahmed Mohamed; Alnagrat, Ahmed Jamah Ahmed
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.v4i2.4889

Abstract

Urban transportation infrastructure, encompassing roads, bridges, and tunnels, is vital for city mobility but remains vulnerable to wear and damage over time. Traditional maintenance methods, which rely on reactive repairs and scheduled inspections, often fall short in preventing sudden failures, resulting in costly disruptions and safety risks. This study examines how artificial intelligence (AI) is revolutionizing infrastructure management through predictive maintenance. By deploying smart sensors and utilizing predictive analytics, AI enables the continuous monitoring of structural health and the proactive identification of potential issues before they escalate into serious failures. The research develops and tests an AI-based predictive maintenance model, which analyzes real-time data from embedded sensors in urban infrastructure to detect anomalies and predict failure patterns. Results indicate that the predictive maintenance model can enhance response times, reduce maintenance costs by 30%, and prevent approximately 92% of unexpected failures. These findings underscore the potential of AI-driven approaches to reduce unplanned disruptions, optimize resource allocation, and extend infrastructure lifespan, ultimately creating safer and more sustainable urban transportation systems. However, challenges in data variability and environmental interference are noted, suggesting areas for future refinement. This study provides a framework for integrating AI in urban infrastructure maintenance, highlighting its potential to transform how cities approach long-term infrastructure health and reliability.
Learning in Immersive Virtual Worlds from the Perspective of Media Didactics Salem, Asma Al Mokhtar Miftah Alhaj; Alfaqi, Rawad Mansour Abdulhafith; Alnagrat, Ahmed Jamah Ahmed
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.v4i2.4893

Abstract

Virtual Reality (VR) technologies are increasingly recognized for their potential to enrich educational settings, yet their integration often emphasizes technological novelty over pedagogical effectiveness. In the domain of media didactics, VR’s value lies not only in its immersive and interactive capabilities but also in its capacity to fulfill specific educational objectives through structured engagement. This study explores the role of immersive VR environments in supporting educational activities by aligning VR’s affordances—such as realism, interactivity, and user engagement—with established didactic principles. The primary objective is to provide a framework that encourages educators to implement VR in ways that are pedagogically sound, thereby enhancing learner engagement and skill acquisition. Using a qualitative approach, the study synthesizes recent literature and analyzes case studies within four key VR applications: training environments, construction tools, exploration experiences, and experimental simulations. Results indicate that VR significantly contributes to experiential learning across these domains, with applications including skill-based training in virtual workshops, exploratory learning through virtual field trips, and controlled experimentation that supports hypothesis testing in virtual worlds. The study concludes that VR holds transformative potential for education; however, its impact is maximized when embedded within a purposeful didactic framework. By aligning VR applications with clear educational goals, VR can foster cognitive and emotional engagement, improving learning outcomes across diverse disciplines.
Air Quality Monitoring System Based Internet Of Things arkhan, muhammad ghozi; Elsi, Zulhipni Reno Saputra
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.v4i2.4924

Abstract

This research aims to develop an Internet of Things (IoT)-based air quality monitoring system using the ESP8266 module along with DHT11 and MQ135 sensors. The system is designed to monitor temperature, humidity, and the concentration of harmful gases in the air, with real-time results accessible through the Blynk application on Android devices. The DHT11 sensor measures temperature and humidity, while the MQ135 sensor detects air quality based on concentrations of harmful gases such as carbon dioxide (CO2) and ammonia. Data from these sensors is transmitted to an IoT platform for real-time display. The research methodology follows a prototype model, starting with planning, system modeling, hardware development, and finally, system testing. During testing, the DHT11 and MQ135 sensors demonstrated accuracy in measuring temperature, humidity, and pollutant levels. Results show that the system functions as expected, with sensors responsive to environmental changes such as increases in temperature or pollutant levels. Additionally, the Blynk platform allows users to monitor air quality remotely and receive notifications if air quality reaches hazardous levels. This system is expected to be applicable in environments that require continuous air quality monitoring, such as hospitals, offices, or other enclosed spaces. The study’s findings indicate that this IoT-based air quality monitoring system effectively detects rapid changes in air quality, contributing to environmental health efforts and raising public awareness about the importance of clean air.

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