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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 Articles sent to the Brilliance may not be published elsewhere.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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Designing AI-Based 3D Animation to Inspire Young Entrepreneurs at Bina Bangsa Wahyudin, Wahyudin; Nugroho, Nurhasan; Munawir, Ahmad
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.v5i2.5227

Abstract

Digital transformation presents significant opportunities to develop young entrepreneurship. However, many university students and novice MSME (Micro, Small, and Medium Enterprises) actors still face barriers such as limited digital literacy and access to relevant information. This study aims to design and produce an AI-based 3D animated educational film as a medium to inspire and foster the entrepreneurial spirit among students. The development process follows the Multimedia Development Life Cycle (MDLC) method, which includes six stages: concept, design, material collecting, assembly, testing, and distribution. Several Artificial Intelligence (AI) technologies were utilized throughout the production, including ChatGPT for scriptwriting, Sora and LTX Studio for scene visualization, ElevenLabs for text-to-speech synthesis, Hedra for automatic lipsyncing, Suno AI for background music generation, and CapCut for final video editing. The final product is an 18-minute animated film titled "Langkah Pertama" (The First Step), which narrates a fictional story of university students who support local MSMEs through digital innovations. Initial evaluations involving targeted student audiences suggest that the film is effective as both an educational and motivational tool. It visually conveys key entrepreneurial values while emotionally engaging viewers. The results of this study demonstrate that AI can be practically applied in multimedia-based learning content development, even within limited production environments. This approach provides a scalable, innovative way to enhance entrepreneurship education in the digital age.
Prototype of Car Washing Automation and Monitoring System Using Outseal PLC and Modbus HMI Putra, Gusti Randa Affinda; Ezwarsyah, Ezwarsyah; Putri, Raihan; Badriana, Badriana; Rosdiana, Rosdiana
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.v5i2.6465

Abstract

Automation technology has significantly improved the efficiency of various sectors, including car washing services, which traditionally require intensive time and labor.This research aims to design and develop a prototype of an automatic car washing system using Outseal PLC and Modbus HMI to enhance the effectiveness and quality of car washing processes.The prototype integrates infrared proximity sensors, DC gearbox motors, DC pumps, DC fans, and a conveyor system controlled by a ladder diagram programmed through Outseal Studio. Wireless monitoring and control are enabled using the DT-06 WiFi module with Modbus TCP/IP protocol. Performance testing was conducted to evaluate system responsiveness, sensor accuracy, and the stability of wireless communication between the PLC and HMI.The system successfully automated all stages of the car washing process, including soap spraying, brushing, rinsing with clean water, and drying, achieving response times ranging from 0.14 to 0.3 seconds. Wireless communication remained stable up to a distance of 16 meters, and the infrared proximity sensors demonstrated high detection accuracy with an error margin of only 2–3.2%.The developed prototype effectively automates the car washing process with accurate detection and responsive control, operating reliably under testing conditions. This system is suitable for small-scale car washing operations and educational laboratory applications, with potential scalability for broader industrial implementation with additional development in advanced brush mechanisms and cloud-based monitoring integration.
An Integration Study of MANET and VANET for Communication in Smart Cities Mayang, Ajeng; Barnadi, Yudi; Suryana, Ase
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.v5i2.6476

Abstract

The development of Internet of Things (IoT) technology has accelerated the development of Smart City, where efficient and real-time communication infrastructure is considered a very important aspect here. Mobile Ad-hoc Networks (MANETs) and Vehicular Ad-hoc Networks (VANETs) topologies offer flexible and adaptive communication models that can respond to dynamic mobility for changes in topology conditions. Generally MANETs are used for pedestrians and stationary sensors, while VANETs are used for vehicles. This study will use both topologies to be simulated for their use if applied in the development of smart cities. In this study, the feasibility and reliability of both topologies, both MANET and VANET will be tested by evaluating performance in terms of Packet Delivery Ratio (PDR) for data transmission reliability, delay (latency) for information timeliness, and throughput for data transfer capacity, which is done using NS-3 as its simulator. The number of nodes used varies from 10 - 100 for each MANET and VANET topology. Simulation results consistently show that networks using both topologies can provide superior performance. The results of this research show that combining the strengths of MANET and VANET is very suitable for future smart city applications. With its ability to provide reliable and efficient communications in highly dynamic urban environments, this technology is one of the promising solutions for building a robust and responsive communication system.
Utilizing TikTok for Digital Branding in Construction: A Case Study of Havana Sulung Setiawan, Dimas; Pamungkas, Ridho; Lenawati, Mei; Asnawi, Noordin; Romadhona, Galang
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.v5i2.6556

Abstract

This study examines the digital branding potential of Mitra Havana Sulung Mandiri, a construction service company in East Java, through the use of TikTok as a promotional platform. The construction industry is traditionally slow in adopting digital media, making this study relevant in exploring new communication strategies. Using observations and interviews, the study identifies the company’s visual strengths—such as project progress updates, before-after visuals, and client testimonials—as effective content for short-form video storytelling. However, internal challenges include limited digital literacy, lack of content production tools, and the absence of standard procedures. External barriers such as skepticism toward social media marketing in the construction field and algorithm changes on TikTok also hinder adoption. Thematic analysis reveals three key needs: utilizing existing visual assets effectively, improving digital skills through internal training, and developing a culture that supports digital transformation. As an initial solution, the study proposes a basic strategy involving educational and behind-the-scenes videos, a simple content production guide, and a 30-day content campaign to test engagement. Collaboration with local creators is also suggested to increase reach. The findings support previous research on the role of short-form video in enhancing brand awareness and trust, particularly in service-based and traditionally conservative industries.
Profit Prediction for Skincare Resellers Using the Exponential Smoothing Method Cahyani, Nita; Irsyada, Rahmat; Firman, Azharil; Inayaturohmat, Fatuh; Pramesti, Retta Farah
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.v5i2.6585

Abstract

This research elucidates the application of the exponential smoothing method in forecasting profit figures for Lutfia MS Glow Skincare. This method was chosen due to its capability to adapt data using the alpha value, along with continual refinement based on exponentially smoothed historical averages. With an explanatory purpose, the study collected profit data from 2020 to 2022 at Lutfia MS Glow Skincare. The single exponential smoothing technique was employed to develop a profit prediction system, enabling the identification of sales trends and evaluation through metrics like Mean Absolute Error (MAE) and Mean Squared Error (MSE). The approach offers simplicity in implementation while providing relatively accurate results, especially for short-term forecasting. This makes it particularly useful in retail and skincare business contexts, where sales figures can be volatile due to seasonal demands or market fluctuations. By utilizing exponential smoothing, the research helps reduce forecasting errors and provides actionable insights for business planning. The result of the analysis showed a reasonably low error margin with a Mean Absolute Percentage Error (MAPE) of 3.65%, indicating high prediction accuracy. The research outcomes furnish skincare resellers and decision-makers with practical guidance in planning inventory, managing supply chains, and executing marketing strategies, ultimately supporting better data-driven decisions in a competitive industry.
Classification Sentiment Toward the Indonesian National Soccer Team on Twitter Using Text Mining Transformation Nugraha, Jie Catur; Zakiyah, Azizah
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.v5i2.6593

Abstract

People now primarily use social media, particularly Twitter, to share their thoughts, feelings, and reactions to events, including in sports like soccer.  By gathering information from the official Twitter account @TimnasIndonesia during the World Cup qualifying phase, this study seeks to examine how the public views the Indonesian national team.  The Support Vector Machine (SVM) approach was used to classify 412 tweets after they had undergone text pre-processing steps such as data cleaning and text transformation.  Three sentiment categories were employed: good, negative, and neutral. With a percentage of 76.7%, neutral sentiment is the most prevalent sentiment, followed by positive sentiment (17.0%) and negative sentiment (6.3%), according to the classification results.  With a precision of 0.83 and a recall of 1.00, the neutral category outperformed the others, according to the model evaluation.  The model's overall accuracy rate of 83% indicates how successful the strategy is.  Still, there are issues with categorizing positive and negative emotions.  There are still a lot of positive tweets that go undetected since positive emotion has a very low recall (0.18) and a high precision (1.00). Thus, it is advised that future studies concentrate on developing more representative text features and enhancing the classification performance of minority categories using methods like oversampling, undersampling, or class weight adjustment. This will help to balance the data distribution and enable the model to classify all sentiment categories more accurately.
A Dental Chatbot Based on IndoBERT with Next Sentence Prediction and Intent Classification Isya, Nadhief Athallah; Rasim, Rasim; Anisyah, Ani
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.v5i2.6620

Abstract

Low public awareness regarding the importance of dental health remains a significant issue in Indonesia. This situation is exacerbated by limited access to consultation services that are easy, fast, affordable, and available at any time. As a result, many dental diseases go undetected at an early stage. Additionally, the tendency to delay dental check-ups is often caused by time constraints and the distance to healthcare facilities, leading many people to avoid consulting with dentists. To address this problem, this research developed a dental health chatbot based on Natural Language Processing (NLP) using IndoBERT. The model was pretrained with the Masked Language Model (MLM) approach and fine-tuned using Next Sentence Prediction (NSP) and intent classification tasks. The dataset was compiled from Indonesian-language dental health articles, symptom–disease sentence pairs, and follow-up questions, all validated by certified dentists. The system was implemented as a web application using React JS for the frontend, Express JS and MySQL for the backend, and integrated with the NLP model through a Flask API. Evaluation results show that the chatbot can provide relevant dental health information, including lightweight consultations to assist in early symptom detection, answer follow-up questions, and generate digital medical records. Expert validation produced an average score of “Good” across the aspects of clarity, relevance, medical accuracy, and completeness, with Likert scale scores ranging from 3.53 to 3.67. This research is expected to contribute as an accessible 24-hour online dental health information service aimed at increasing public knowledge and awareness.
A Mobile-Based Expert System for Glaucoma Diagnosis Using the Naive Bayes Algorithm Bahar, Amelia; Yasir, Fajar Novriansyah; Sukmawati, Sukmawati
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.v5i2.6633

Abstract

This study aims to develop a mobile-based expert system application for early diagnosis of glaucoma using the Naive Bayes algorithm. The application is designed to help users recognize early symptoms of glaucoma, provide preliminary information, and increase public awareness to reduce the risk of vision loss or blindness. The application was developed using the Dart programming language, the Flutter framework, and Firebase as the database platform. The research method employed is Research and Development (R&D), utilizing the 4D development model, which consists of four stages: Define, Design, Develop, and Dissemination. To evaluate the functionality and effectiveness of the application, both black-box testing and expert validation were conducted. The Naive Bayes algorithm implemented in the application demonstrated a high accuracy rate of 97.50%, indicating strong reliability in recognizing symptom patterns and producing appropriate diagnostic predictions based on user input. Furthermore, the System Usability Scale (SUS) was used to assess the application's usability, yielding a high average score of 97.5%, reflecting excellent ease of use and user satisfaction. In addition, content validation by subject matter experts resulted in an average feasibility score of 98.07%, indicating that the application is highly suitable for public use in supporting early screening and diagnosis of glaucoma.
Development of Sustainable Curriculum Management System with Server-Driven Go and Next.js Fitrianto, Adi; Kangadi, Farel Artanto; Rizki, Sestri Novia; Lestari, Verra Budhi; Jaya, Eko Amri
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.v5i2.5381

Abstract

Curriculum management is a crucial aspect of educational administration, encompassing the planning, organization, and continuous updating of course data and curriculum structures. In many educational institutions, including those in Jakarta, this process still encounters challenges such as inefficiency, data duplication, inconsistent information flow, and high system maintenance overhead. These issues often result in delays, administrative burdens, and reduced productivity. This research aims to develop a sustainable curriculum management system by leveraging Go technology on the backend and Next.js on the frontend, adopting a server-driven approach to minimize processing loads on client devices, improve resource efficiency, and ensure faster data delivery. The system development method employs an iterative approach with the integration of ten supporting technologies, including server-side data fetching, Backend for Frontend (BFF), OpenAPI 3 with code generation, Redis caching, tag-based revalidation, Server-Sent Events (SSE), component-based UI, image optimization, and asynchronous job queues for heavy tasks. The system integrates course and curriculum name management into a single, responsive, secure, and easily extensible platform. Implementation results demonstrate that the approach can reduce maintenance complexity, improve data consistency, and significantly enhance application response time under varying loads. Furthermore, the system supports sustainable software engineering principles, enabling scalability and adaptability to future needs. Consequently, it offers an effective, long-term solution for curriculum management, contributing to operational efficiency and the overall quality of academic administration in educational institutions across Jakarta.
Website-Based Text Encryption Simulation with Hill Chiper Sukiman, T. Sukma Achriadi; Zulfia, Anni; Karima, Annisa; Ulya, Athiyatul; Rizky, Muharratul Mina
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.v5i2.5757

Abstract

Data security has become increasingly crucial in the modern digital era, where almost all types of information ranging from text, images, to audio are stored and exchanged in digital form through open networks. The rapid growth of internet-based communication makes data highly vulnerable to interception, modification, or misuse by unauthorized parties. Cryptography is one of the most effective solutions to address these challenges. Among the classical cryptographic techniques, the Hill Cipher remains relevant today because it is based on linear algebra and matrix transformations, which provide a strong mathematical foundation and can be adapted for modern computational implementation. In this study, a web-based application was developed using the Python Flask framework to implement the Hill Cipher algorithm. The application enables users to perform both encryption and decryption of text and images through an interactive interface. Users can input plaintext and key matrices, and the system processes the data to produce encrypted or decrypted outputs in real time. This design not only demonstrates the practicality of applying classical cryptographic concepts with contemporary web technologies but also serves as a valuable educational tool. The results show that the application performs effectively, producing accurate outputs, while also supporting user learning in understanding encryption–decryption processes and guiding efforts to secure digital information.

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