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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 Articles sent to the Brilliance may not be published elsewhere.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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Evaluation of Employee Payroll Website Quality Using the WebQual 4.0 Model at SPBU 23.301.34 Olivia, Olivia; Farisi, Ahmad
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.v6i1.7596

Abstract

The rapid adoption of web-based information systems in human resource management has increased the importance of evaluating website quality from the user perspective. This study aims to evaluate the quality of the employee payroll website at SPBU 23.301.34 using the WebQual 4.0 model, which consists of usability, information quality, and service interaction quality, as well as user satisfaction. A quantitative approach was employed using a descriptive and verificative research design. Data were collected through questionnaires distributed to all active users of the payroll website, totaling 24 respondents, supported by observations and interviews. The collected data were analyzed using descriptive statistics, validity and reliability testing, and mean value analysis for each indicator and dimension. The results indicate that the overall quality of the payroll website is categorized as good. The usability and information quality dimensions obtained the highest mean scores, indicating that the website is easy to use and provides clear, accurate, and understandable payroll information. In contrast, service interaction quality received the lowest score, mainly related to system response speed and occasional technical issues during peak usage periods. Despite these limitations, user satisfaction remained at a moderately satisfied to satisfied level, demonstrating that the website provides practical benefits for employees. These findings suggest that the payroll website contributes to improving transparency and efficiency in payroll administration. However, improvements in system performance, stability, and data update processes are recommended to further enhance user experience and service quality.
Classification of Cassava Leaf Diseases Using ResNet50 CNN Architecture Based on Digital Images Malik, Maulana; Wijaya, Novan
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.v6i1.7686

Abstract

Cassava (Manihot esculenta) is an important agricultural commodity in Indonesia, but its productivity can decline due to leaf diseases such as Cassava Mosaic Disease (CMD), Cassava Green Mottle (CGM), Cassava Bacterial Blight (CBB), and Cassava Brown Streak Disease (CBSD). These four diseases exhibit overlapping visual symptoms such as chlorosis, spots, and leaf discoloration, making them difficult to distinguish manually. This study aims to create a digital- based cassava leaf image classification system using the Convolutional Neural Network (CNN) algorithm and ResNet50 architecture. The dataset used consists of 9,436 cassava leaf images taken from the TensorFlow platform and processed through resizing, normalization, selective augmentation, and the application of transfer learning. The experiment compared various optimizer configurations, learning rates, batch sizes, and balanced and unbalanced dataset scenarios. The evaluation was conducted using accuracy, precision, recall, and F1-score. The results show that the best performance was obtained on an unbalanced dataset using the Adam optimizer (learning rate 0.001; batch size 64) with an accuracy of 80.69% and an F1-score of 79.76%. Meanwhile, balancing the dataset actually reduced performance to an accuracy of 77.14% and an F1-score of 76.48%. Analysis of the loss curve and confusion matrix confirmed that the natural data distribution provided more stable generalization, although misclassification still occurred in classes with similar visual symptoms. These findings indicate that ResNet50 is effective for classifying cassava leaf diseases and has the potential to support early detection in digital agriculture practices.
Implementation of C5.0 Algorithm in Cement Stock and Purchase Management at PT. Maktal Maulida, Rezky Salman; Zakiah, Azizah
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.v6i1.7744

Abstract

Stock management is a crucial activity in the supply chain of any company, including PT. Maktal, which operates in cement distribution. The stock management system, which still relies on experience and manual methods, has the potential to cause a mismatch between demand and supply, ultimately leading to excessive inventory costs (overstock) or product shortages (stockout). The implementation of Machine Learning offers a solution to enhance the accuracy of stock needs planning. This study aims to develop and compare the performance of machine learning models, specifically the Decision Tree (C5.0) and Random Forest algorithms, in predicting the category of cement stock needs (Low, Medium, High) based on historical transaction data. The data used are historical cement sales and ordering transactions of PT. Maktal from 2020 to 2024. The stock quantity data was converted into categorical variables (Low, Medium, High) through a discretization process. Both algorithms were tested and evaluated for their performance using accuracy, precision, recall, and F1-score metrics through a cross-validation test. The comparative results indicate that the Random Forest algorithm provides the best prediction performance with an accuracy level reaching 79.91%. This performance is significantly higher than that of the Decision Tree algorithm. Feature importance analysis identified that the Purpose (customer type) and Month variables are the most influential predictors of the stock needs category. The Random Forest model proved to be effective and reliable as a data-driven decision support system to optimize stock planning and cement purchasing at PT. Maktal, reducing the risk of losses due to demand uncertainty.
Implementation of Prototype Method in a Web-Based Educational Word Guessing Game with Melodic Elements Prayitno, Gunawan; Danomira, Yuspin Yokbet
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.v6i1.7822

Abstract

The rapid development of digital technology has encouraged the use of interactive learning media, particularly web-based educational games, to support engaging learning experiences. This study aims to design, implement, and evaluate a web-based educational word guessing game integrated with melodic elements as an alternative digital learning medium. The study employs the prototype development method, which enables iterative system development through continuous user feedback. The research stages include needs analysis, prototype design, system development, and system testing and evaluation. System evaluation was conducted through functional testing using a black-box approach to ensure that all features operated according to predefined requirements, as well as descriptive usability evaluation to assess ease of use and user interaction. The results indicate that the developed system functions properly, with core features such as word guessing mechanics, score management, and melodic feedback operating as intended. The usability evaluation shows that the game interface is easy to understand and provides a clear gameplay flow for users. These findings suggest that the developed web-based educational word guessing game has the potential to serve as an interactive and engaging digital learning medium. This study contributes to the development of educational games by demonstrating the application of the prototype method and the integration of melodic elements to enhance user interaction in web-based learning environments.

Page 55 of 55 | Total Record : 544

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