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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 Articles sent to the Brilliance may not be published elsewhere.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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Enhancing Transaction Smoothness with QRIS Technology: The Role of DIGI by Bank BJB in Optimizing Customer Experience at the Banjar Branch Faizal, Riza; Marlina, Lina; Setiawan, Ati S
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.v4i2.4928

Abstract

The implementation of the Quick Response Code Indonesian Standard (QRIS) in the DIGI by bank bjb application faces challenges in enhancing the transaction flow for customers at the Banjar Branch. The primary issue is the demand for ease, speed, and efficiency in digital transactions, while aspects such as promotions and QRIS scanning capability across various merchants remain suboptimal. This study aims to examine the impact of QRIS usage within the DIGI application on the transaction flow for bank bjb customers. A quantitative research method with an explanatory survey approach was employed, involving 100 respondents who are bank bjb customers in the Banjar area. Data was collected using a Likert-scale questionnaire and analyzed using simple linear regression. The results indicate a significant positive influence of QRIS usage on transaction flow, with a t-value that demonstrates a strong correlation. The average customer satisfaction score indicated a “Very Good” criterion. These findings imply that the QRIS feature in the DIGI by bank bjb application can enhance customer transaction experiences; however, the bank needs to improve promotional features and expand QRIS scanning capabilities to further increase customer satisfaction and the competitiveness of its digital services.
Implementation and Performance Analysis of Internet Networks for RT/RW Using MikroTik and OpenWRT in Simalidu Koto Salak Dhamasraya Putra, Aldo Aditya; Dewi, Ratna; Setiawan, Herry
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.v4i2.4940

Abstract

This research analyzes the implementation and performance of RT/RW Net Internet networks utilizing MikroTik and OpenWrt in Simalidu Koto Salak, Dharmasraya, addressing the significant challenges posed by the region's telecommunications infrastructure. The primary objective is to evaluate critical Quality of Service (QoS) parameters that greatly influence user experience, specifically focusing on bandwidth, throughput, delay, jitter, and packet loss. Employing a systematic methodology, the study begins with site surveys to assess geographical and infrastructural contexts, followed by the deployment of a Point-To-Point network system designed to ensure optimal signal quality and efficient user management through MikroTik devices. Findings reveal that QoS in the RT/RW Net network is considerably affected by both technical configurations and the quality of the signal, culminating in a QoS score of 3.5, which corresponds to a 75% performance rating. These results indicate that, while the implemented network solution meets basic connectivity needs, further optimization is crucial to enhance user satisfaction and overall performance. Consequently, the study offers several recommendations aimed at improving internet service quality in similar environments, highlighting the necessity of continuous evaluation and adaptation of network configurations to meet the evolving demands of users. Furthermore, it suggests that future research could delve into advanced technologies and methodologies to further enhance network performance and user experience, particularly in rural areas, thereby effectively addressing connectivity challenges and ensuring that the benefits of reliable internet access are widely distributed among local communities.
Solar Purchase Volume Prediction Using The K-Nearest Neighbor Algorithm Based On Backward Elimination Prasetyo, Aries Alfian; Pramono, Yudi; Ulfiyah, Laily; Fattah, Misbakhul
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.v4i2.4964

Abstract

The profit earned by a Public Filling Station or gas station comes from the purchase of fuel per period and sales in accordance with the volume amount. So the exact purchase volume will determine the turnover of each month. But the profit or turnover is often irregular, there are several causes such as the price of fuel that tends to change, the volume of orders that are not in accordance with consumer demand. With these prediction methods, the expected turnover is increased with more efficient purchases. This research was conducted to study about k-NN Algorithm and then apply k-NN Algorithm in data prediction. The data used are secondary data in the form of data on the number of purchases of BBM in volume liter in the period January 2012 - December 2024. The k values used are k = 1, K = 4, k = 5 and k = 7. Before calculation with k = 1 is done, determined the data of training and data testing, in this research determined as much 70% training data and 30% for data testing. Then the initial cluster determination of the training data based on the interval class. While the cluster in the data testing is determined based on testing with K = 4, k = 5 and k = 7. From the process of analysis and evaluation of the research predicted the volume of fuel purchases using the data ransed dataset that processed data into multivariate data, the process of analysis using K-NN method using 2,3,4 and 5 periods produce the smallest K located in the period to 3, so that the 3rd period will be predicted by K-NN based backward elimination. With the aim of finding the best method to predict the volume of fuel purchases, generate predictions with backward elimination, that the attribute weights in the period xt - 3 and in the period xt 1 selected as the reference in the prediction process, since the weight is 1. K = 13 is the K best way to perform the Analyzing process with K-NN for the prediction of fuel purchase volume, with K = 4 value of 45556,788. So in the analysis and prediction of oil fuel purchasing volume data, for the type of diesel, K is best K = 13 with K-NN analysis method with backward elimination process. The above results show that xt3 or week 3 and week xt1 to 1 in the last period of 2024 can be used as a reference in the purchase in the next year that is 2025.
Implementation of Web-based Online Examination for High School Students Khumaini, Hayatullah; Pratiwi, Fitri; Daulay, Jamil; Putra, Al Malikul Ikhwanda; Rubiati, Nur
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.v4i2.4965

Abstract

Examination is a form of evaluation of the learning process to measure the achievement of student intelligence in schools. The implementation of conventional exams that are often carried out by schools has the potential for fraud both from students who cheat during the exam or leak exam questions before the exam takes place and also the operational costs that must be incurred by schools to duplicate questions and coupled with teachers needing additional time to check student exam results. In addition to being carried out in writing or orally, exams can also be carried out using computer aids. With the help of this study, teachers will be able to create questions without worrying that they will be leaked to students before the test and share them with other educators teaching the same subject. Students should be able to receive exam results right away after the test is over, and teachers won't have to worry about checking the results because they will be sent to them automatically. For schools, the costs incurred for paper can be reduced with this online exam system. The results of this research are in the form of a web-based application with the concept of client server using the help of a framework that already supports the model, view and controller system. This application has users, where there are several users with differentiators in the form of access rights.
Digital Transformation in Banking Services: The Impact of Mobile Banking on Customer Satisfaction at Islamic Banks in Tasikmalaya City Mulyati, Sri; Septiani, Nendah; Marlina, Lina
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.v4i2.4967

Abstract

This study aims to analyze the impact of mobile banking services on customer satisfaction at Bank Syariah Indonesia in Tasikmalaya City. The methodology applied is descriptive and verification based in a quantitative form. The study sample was determined using the Slovin method, involving a number of respondents who are customers of Bank Syariah Indonesia in the specified area. Data collection was conducted through literature review and questionnaires. To analyze the effect of mobile banking services on customer satisfaction, this study utilizes simple linear regression analysis with a set significance level. The analysis results indicate that mobile banking services have a positive and significant impact on customer satisfaction. Based on the coefficient of determination test, it was found that a majority of the variation in customer satisfaction can be explained by the use of mobile banking services, while the remaining portion is influenced by other factors not examined in this study. The statistical test results support a positive and significant effect between mobile banking services and customer satisfaction, reinforcing the relevance of these services in enhancing customer satisfaction at Bank Syariah Indonesia in Tasikmalaya City. The implications of continuous development of BSI Mobile enhances customer loyalty, strengthens competitiveness, and maintains Bank Syariah Indonesia's position amidst digital competition.
Comparing MCDM Methods for Assessing the Lecturer Performance Index at Dipa Makassar University Suryani, Suryani; Patasik, Madyana; Zeannyfer, Stiffany Lourens; Syahputri, Andhiny Nurakzhany
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.v4i2.4979

Abstract

Assessing or evaluating the Lecturer's Performance, presented through the Lecturer Performance Index, is a crucial element in the university system that impacts the quality of The University's Three Main Purposes, which include teaching, research, and community service. To ensure objective and accurate evaluations, methods that can accommodate various relevant criteria are needed. One increasingly popular approach is the Multi-Criteria Decision Making (MCDM) method, which allows for evaluating and comparing alternatives based on multiple criteria. This research compares several MCDM methods, namely Weighted Product (WP), Simple Additive Weighting (SAW), and Multi-Objective Optimization by Ratio Analysis (MOORA), used to assess the lecturer's performance at Dipa University Makassar. The WP method can handle criteria with different units, SAW is simpler and easier to apply, while MOORA offers a more comprehensive analysis. This study also identifies challenges in assessing the lecturer's performance, such as the influence of students' subjective evaluations that may lead to bias, as well as the addition of several of the lecturer's performance evaluation criteria such as teaching innovation, student mentoring, international and national journal publications, internal publications, and book publications. Additionally, self-development criteria based on academic fields are considered. The findings of this research are expected to provide insights into effective MCDM methods for the lecturer's performance evaluation and offer recommendations for educational institutions to choose the appropriate and transparent evaluation method. By using MCDM, the objectivity and accuracy of the lecturer's performance evaluations can be improved, biases can be reduced, and contributions can be made toward developing more fair and systematic evaluation standards.
Optimizing Customer Purchase Insights: Apriori Algorithm for Effective Product Bundle Recommendations Kaban, Ekinnisura; Darmawiguna, I Gede Mahendra; Kesiman, Made Windu Antara
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.v4i2.4981

Abstract

A retail store faces significant challenges in crafting effective sales strategies, particularly in designing promotional product bundles. To address this, the store leverages transaction data to analyze customer purchasing patterns, aiming to uncover products frequently bought together. This study employs data mining techniques, specifically the Apriori algorithm, to identify co-purchasing behaviors using 49,316 transaction records collected from January to June 2024. After thorough data cleaning and transformation, the Apriori algorithm identified 877 itemsets, spanning from frequent 1-itemsets to 4-itemsets. By setting a minimum support threshold of 0.003, the analysis narrowed down to 343 significant itemsets, including 325 frequent 1-itemsets and 18 frequent 2-itemsets, which served as the basis for generating association rules. Initially, 36 association rules were derived, highlighting various product relationships. To focus on impactful insights, the rules were filtered using a minimum confidence level of 0.5, yielding 3 highly relevant rules with lift ratios exceeding 1, indicating strong associations between antecedent and consequent products. These insights enable the store to design targeted promotional bundles, optimize product placement, and enhance overall sales performance. Additionally, this study demonstrates how data-driven strategies can provide a competitive edge by aligning with customer purchasing behaviors. To ensure continuous improvement, a Python-based system was developed, empowering the store to independently analyze transaction data and refine sales strategies in real time, adapting to evolving purchasing patterns as the dataset grows.
Predictive Models for Interest Rate Forecasting Using Machine Learning: A Comparative Analysis and Practical Application Salem, Abdorwf A Mohamed; Albourawi, Amaal Jummah Abdullah
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.v4i2.4983

Abstract

Forecasting interest rates is a fundamental task in financial planning, investment strategies, and policy-making. Traditional statistical models, while widely used, often fail to adequately capture complex non-linear relationships and temporal dependencies inherent in financial data. This study addresses these limitations by exploring the potential of machine learning models to improve the accuracy and reliability of interest rate forecasts. The primary objective of this research is to evaluate and compare the performance of multiple machine learning models, including linear regression, support vector machines, and deep learning techniques, in predicting interest rate trends. Historical data spanning two decades was collected and preprocessed, ensuring data quality and consistency. The models were trained and tested on this dataset using well-defined evaluation metrics such as mean absolute error and root mean squared error to ensure robust performance assessments. The results revealed that machine learning approaches, particularly deep learning models, outperformed traditional methods in capturing complex patterns and delivering more accurate forecasts. The findings further discuss the practical implications of implementing machine learning techniques in real-world financial contexts, highlighting both opportunities and challenges. In conclusion, this study provides actionable insights and a robust framework for integrating machine learning into interest rate forecasting, contributing to the advancement of predictive modeling in finance.
AI Implementation in Knowledge Management for Online News Media Case Study: GOnews.id Mundzir, Ahmad; Purnama, Diki Gita; Mayasari, Iin
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.v4i2.4995

Abstract

This study examines the role of artificial intelligence (AI) in enhancing knowledge management in the online news media industry, with a focus on GOnews.id as a case study. The industry faces unique challenges, including real-time information delivery and collaboration among dispersed teams. This research evaluates the impact of AI in addressing these challenges, improving operational efficiency, and enhancing content quality. Using qualitative methods, including in-depth interviews and document analysis, the study applied a thematic approach with NVivo 12 software. The findings reveal that AI facilitates faster information access, supports team collaboration, and maintains journalistic standards through automated editing tools. However, limitations such as high infrastructure costs, employee resistance, and AI's inability to fully grasp local contexts present challenges to its implementation. The study emphasizes the importance of using AI as a supportive tool rather than replacing human creativity and judgment. Key implications include AI's potential to enhance organizational competitiveness and responsiveness to audience trends. Recommendations include investing in robust infrastructure, providing employee training, and ensuring human oversight to balance technological efficiency with editorial nuance. By addressing these issues, the study provides valuable insights for media organizations seeking to optimize knowledge management strategies and offers a framework for leveraging AI to meet the demands of modern digital journalism.
Implementation of Docker Platform for Integration Of Thesis Information System And Web-Based Thesis Title Checking Information System In Informatics Study Program Ryanto, Rizky Adi; Julkarnain, M.; Yuliadi, Yuliadi; Sofya, Nora Dery; Oktavia, Siska Atmawan
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.v4i2.5011

Abstract

The use of technology-based information systems not only improves operational efficiency but also enriches the learning and teaching experience. In this context, the management of theses, as an essential part of higher education programs, is one area that receives special attention. Based on the results of the observations, the Informatics Study Program registration process and scheduling of proposal seminars and final project trials are currently carried out conventionally. Meanwhile, the Informatics Study Program already has two previous systems, namely the thesis title checking system and the registration system and final project scheduling. However, they still need to be integrated and implemented on the server so the registration process is carried out conventionally. This study uses the Docker platform to discuss integrating the thesis information system and title-checking system in the Informatics Study Program at the Sumbawa University of Technology. The main goal is to improve operational efficiency and user experience, especially for students and teaching staff. This study covers various steps through the Software Development Life Cycle (SDLC) approach and qualitative methods, from analyzing the running system and proposals to implementing Docker as a containerization platform. This integration brings significant changes, uniting the thesis title-checking and registration processes in one integrated platform. The results show increased administrative efficiency, more accessible access, and more focused use. However, this study also encourages further development for system maintenance, data security, and improvements according to user feedback to improve system functionality and resilience in the future.

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