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 Articles sent to the Brilliance may not be published elsewhere.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
 24   25   26   27   28 
System monitoring Soil PH moisture based IoT Ula, Mutammimul; Ezwarsyah, Ezwarsyah; Saptari, Mochamad Ari; Bakhtiar, Bakhtiar; Multazam, T
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.v5i1.5974

Abstract

Soil moisture is a water content that wets the soil pores either partially or completely. So far, the process of checking soil moisture is still done manually, where farmers must come directly to the field. To help the process of checking soil pH, this researcher designed and built a soil pH moisture monitoring system based on the Internet of Things which aims to facilitate the process of monitoring the results of soil values ??including pH and moisture levels. In implementing the development of this monitoring system, it was designed using a soil moisture sensor and pH sensor integrated with an Internet of Things (IoT)-based platform, allowing users to monitor soil conditions in real-time via mobile or web applications. With accurate and continuous data, farmers can take quick steps in irrigating and amending soil pH, thereby increasing plant productivity and maintaining soil health. This system implements an Arduino UNO microcontroller, transmitter, YL-69 soil moisture sensor, and LCD as part of the soil monitoring system. Based on the results of the design and testing of this soil moisture monitoring system, users can get information on the condition of the soil pH moisture content. The test results show that this system has high measurement accuracy and easy data access, making it a potential solution to support sustainable agriculture.
Decision Support System for Potential Stock Selection Recommendations Using AHP and Profile Matching Methods Sahputra, Ilham; Ilhadi, Veri; Pratama, Angga; Syukriah, Syukriah; Arifa, Tiara Minda
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.v5i1.5981

Abstract

This study presents the design and implementation of a Decision Support System (DSS) aimed at facilitating the selection of potential banking stocks by novice investors. The system integrates two well-established decision-making methodologies: the Analytical Hierarchy Process (AHP) and Profile Matching. The objective is to provide a structured, data-driven approach that assists users in making informed and objective investment decisions based on critical financial performance indicators. These indicators include Price to Earnings Ratio (PER), Price to Book Value (PBV), Return on Assets (ROA), Return on Equity (ROE), Earnings Per Share (EPS), Book Value Per Share (BVPS), Debt Ratio (DR), and Dividend Yield (DY). In this system, AHP is employed to calculate the relative weight or importance of each financial criterion through pairwise comparisons, incorporating users judgment in the weighting process. Once the weights are determined, the Profile Matching method is used to assess and rank the alternative banking stocks based on how closely they align with the ideal profile defined by the criteria. The results of the analysis identified Bank Mandiri (BMRI) as the top-ranked stock, followed by Bank Rakyat Indonesia (BBRI) and Bank Central Asia (BBCA), indicating their strong fundamental performance according to the selected indicators. To validate the system's functionality, black-box testing was conducted on 21 different modules, all of which yielded valid outcomes. This confirms that the application operates correctly and reliably. Overall, the study concludes that the DSS is effective, user-friendly, and valuable as a decision support tool, especially for beginner investors targeting the banking sub-sector.
Analysis Comparative of Performance Optimization Techniques for PHP Framework Testing: Laravel, CodeIgniter, Symfony: Analisis Perbandingan Teknik Optimasi Performa Untuk Pengujian Framework PHP: Laravel, CodeIgniter, Symfony Putra, Fauzan Prasetyo Eka; Zulfikri, Achmad; Rohman, Abd; Alim, Royfal
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.v5i1.5989

Abstract

Web application performance significantly impacts digital services in sectors like e-commerce, education, and healthcare, where poor response time (>100 ms) can reduce user satisfaction by up to 20% and cause revenue losses. Despite numerous studies on PHP frameworks, comprehensive comparisons integrating optimization techniques remain limited. This research empirically evaluates the performance of Laravel, CodeIgniter, and Symfony, focusing on response time, throughput, and error rate before and after applying optimization techniques. Load and stress testing was conducted using Apache JMeter, Xdebug, and New Relic on a student management system with a MySQL database (10,000 records), simulating realistic scenarios from 1 to 100 users, mimicking e-commerce or educational platforms. Optimization techniques included caching (e.g., route caching in Laravel, query caching in CodeIgniter), database optimization (indexing, eager loading), and code refactoring. Results revealed CodeIgniter achieved the lowest response time (16.49 ms, 41.4% reduction) and highest throughput, ideal for lightweight applications like news portals. Laravel showed the greatest improvement under high load (35.1% reduction, from 118.34 ms to 76.80 ms), suitable for e-commerce platforms. Symfony demonstrated stable performance (20.0%–25.0% reduction), fitting enterprise applications with complex APIs. This study provides practical guidance for developers in selecting PHP frameworks based on project requirements and offers optimization recommendations to enhance web application efficiency and scalability.
The Analysis of the Impact of AI Utilization in Creating Ghibli-Style Visual Effects on the Creative Industry: Analisis Dampak Penggunaan Kecerdasan Buatan (AI) dalam Pembuatan Efek Visual Gaya Ghibli terhadap Industri Kreatif Arifin, M. Nazir; Mahmud, Moch. Amir; Haris, Farras Maulana; Ramadhan, Moh. Hairul
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.v5i1.5993

Abstract

The emergence of artificial intelligence (AI) has opened new creative possibilities, particularly in the visual arts. This study investigates the impact of utilizing AI-generated illustrations in the style of Studio Ghibli on the creative industry. The research aims to explore how such technology influences creative workflows, aesthetic values, and public reception. A qualitative approach was employed through interviews with illustrators, animation professionals, and digital content creators, combined with a case study analysis of AI-generated Ghibli-style artworks. The findings indicate that AI tools not only accelerate the production process but also inspire new forms of artistic expression. However, concerns were raised regarding originality, intellectual property rights, and the potential displacement of human artists. Despite these challenges, most respondents acknowledged the potential of AI as a collaborative tool rather than a replacement. This study concludes that the integration of Ghibli-style AI art into the creative industry can be a catalyst for innovation while also demanding adaptive strategies from practitioners and policy makers alike.
Analysis of Google Play Store User Sentiment Towards Application X Using the SVM Algorithm Arifin, Mohammad Nazir; Amir Hamzah; Huda, Moh. Abroril; Hasanah, Nor
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.v5i1.6024

Abstract

This study uses the Support Vector Machine (SVM) algorithm to examine user opinions about the X app on the Google Play Store. Data went through preprocessing steps like cleaning, casefolding, tokenizing, filtering, and sentence reconstruction from 5,000 reviews that were gathered via web scraping. Using a lexicon-based method, sentiment labeling was carried out, categorizing reviews into three groups: neutral, negative, and positive. The findings indicated that 49.4% of respondents had unfavorable opinion, followed by neutral (29.8%) and positive (20.8%). An SVM model with an accuracy of 86.3% was generated by feature extraction using Bag of Words with CountVectorizer. The neutral class had the highest recall (0.96) and the negative class the highest precision (0.95). With an F1-score of 0.78, the positive class performed the worst, most likely as a result of data imbalance. Despite difficulties in categorizing minority classes, this study shows that SVM is useful in sentiment analysis.
Design Of Automatic Laptop Cooling System Using Ds18b20 Temperature Sensor Based On Arduino Nano Oktrison, oktrison; Sipahutar, Erwinsyah; Candra, Rudi Arif; Budiansyah, Arie
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.v5i1.6073

Abstract

Technological developments in the field of automation provide opportunities to improve efficiency and comfort in the operation of electronic devices. This research aims to design and implement an automatic cooling system on a laptop that uses an Arduino Nano-based DS18B20 temperature sensor. The system is designed to automatically regulate the laptop temperature by monitoring the temperature in real-time, and activating the cooling fan through a relay when the temperature reaches 33°C or more. This research method includes hardware design that involves the use of Arduino Nano as a microcontroller, a DS18B20 temperature sensor to detect temperature changes, and a relay to control the cooling fan. The software was developed using the Arduino programming language (C++) to process the data from the sensors and manage the work of the cooling system automatically. The test results show that the system can accurately detect the laptop temperature and respond in real-time by turning on the cooling fan when the temperature exceeds the 33°C limit. The system proved to be effective in preventing overheating, keeping the device temperature within safe limits, and optimizing power consumption by turning off the fan when the temperature returns to stable.
The Effectiveness of Machine Learning Techniques in Anomaly Detection for Cyberattack Prevention: Systematic Literature Review 2020-2025 Budiansyah, Arie; Zulfan, Zulfan; Nizamuddin, Nizamuddin; Candra, Rudi Arif; Ilham, Dirja Nur; Nazaruddin, Nazaruddin
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.v5i1.6124

Abstract

As digital technology evolves, cyberattacks are becoming more diverse and difficult to detect. Conventional detection methods are often incapable of recognizing new and sophisticated attack patterns. Therefore, machine learning techniques are starting to be widely used because of their ability to study data patterns and detect unusual or anomalous activities. This study aims to systematically examine the effectiveness of various machine learning techniques in detecting anomalies as an effort to prevent cyberattacks. The research was conducted using the Systematic Literature Review (SLR) method on 20 scientific articles from reputable journals published between 2020 and 2025. The articles were selected through a search, selection, and analysis process following PRISMA guidelines. The results of the study show that algorithms such as Random Forest and Decision Tree consistently provide accurate detection results, especially in network systems and the Internet of Things (IoT). Meanwhile, deep learning techniques such as CNN and LSTM show high performance in handling large and complex data. However, challenges are still found in terms of data imbalances, high computing requirements, and lack of model interpretability. The conclusions of this study show that machine learning techniques are very promising for anomaly detection in cybersecurity, but an adaptive and easy-to-explain approach is needed. Researchers are further advised to develop models that are more efficient, transparent, and able to adapt to evolving cyber threats.
Firewall Implementation as a Computer Network Security Strategy for Data Protection Putra, Fauzan Prasetyo Eka; Dafid, Mohammat; Syafi’i, Imam
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.v5i1.6162

Abstract

Computer network security plays a crucial role in ensuring the integrity, confidentiality, and availability of data in the modern digital era. One of the primary tools used to safeguard networks from unauthorized access and external threats is the firewall. This paper explores the significance of firewalls in protecting network systems by examining their functions, various types, and capability to regulate data traffic. Firewalls work by enforcing specific rules to limit access, effectively reducing the risk of cyber threats such as malware, hacking attempts, and unauthorized intrusions. The study also involves practical observation of firewall implementation in a real network setup to evaluate their efficiency in mitigating security risks. Findings indicate that properly configured firewalls can considerably strengthen a network’s defense. Nevertheless, relying solely on firewalls is insufficient. They should be supported by additional security technologies and best practices to achieve comprehensive network protection. In conclusion, firewalls are essential elements in upholding the reliability and safety of computer networks.
Lobster Growth Monitoring with AI-Based Computer Vision Using SVM and Neural Network Suhendri, Suhendri; 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.v5i1.6210

Abstract

A modern agricultural and aquaculture technique today heavily relies on computer assistance. Computers aid in the analysis, identification, and regulation of feeding patterns, making the process more effective. For example, lobster farming is now predominantly conducted using pond-based methods, known as aquaculture, rather than sourcing lobsters from the wild. This is because lobsters are highly sensitive creatures, and failing to replicate their natural habitat can lead to crop failure. Several factors influence lobster farming conditions, including water quality, feed quantity, and lobster species. Another critical factor is disease outbreaks, which can spread rapidly due to the high lobster density in a single pond. Managing these conditions manually is impractical due to the large number of ponds and the need to replicate natural habitat conditions accurately. To address these challenges, a monitoring mechanism utilizing artificial intelligence (AI)-based image processing is implemented. AI methods can manipulate environmental conditions to closely resemble a lobster’s natural habitat by monitoring pH levels, determining gender, and assessing health status. Data accuracy is ensured using two algorithmic approaches. Experimental results show that the application is designed as a GUI with simple features, making it user-friendly for farmers and the general public. This application was tested using a sample of 200 lobsters, achieving a data accuracy rate of 95% with the SVM algorithm and 85% with the Neural Network algorithm. The application can identify lobster species, size, and potential diseases affecting them.
The Design of Baby Equipment Rental Website Using Laravel Framework at Rafana Rent Store Nurhaliza, Sitti; B, Isdayani; Siswanto, Rahmat
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.v5i1.6213

Abstract

Technological developments have encouraged various business sectors to adapt to digitalization, including the baby equipment rental sector. This study aims to design and build a web-based baby equipment rental information system using the Laravel Framework at the Rafana Rent Store in Palopo City. The method used is a qualitative descriptive approach using a prototype development model. Data was obtained through interviews, observations, and documentation of the rental process carried out manually. The results of this study indicate that the system created can overcome various problems in the manual rental process, such as delays in service, recording, which is still done manually in notebooks, and lack of efficiency in payment confirmation. This system provides main features such as online ordering, uploading proof of payment, and managing stock of goods. Through the trials, the system was considered to facilitate customers in carrying out the rental process, which can be done anytime and anywhere, and help shop owners manage transaction data more accurately and provide efficient service to customers. Implementing this system is expected to improve the quality of service and speed up and simplify the rental process. The Rafana Rent Store can manage its business more professionally and be ready to face the digitalization era. This system can also be an example of the application of information technology in micro businesses.

Page 26 of 55 | Total Record : 544

 24   25   26   27   28 

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