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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 Articles sent to the Brilliance may not be published elsewhere.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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Implementation of the Naive Bayes Algorithm for Death Due to Heart Failure Using Rapid Miner Surojudin, Nurhadi; Ermanto, Ermanto; Danny, Muhtajuddin; Pratama, Suria
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4136

Abstract

Until now there is no treatment that can specifically treat heart failure problems. Heart failure treatment only functions to control symptoms, improve quality of life so that patients can carry out normal activities, and reduce the risk of complications due to heart failure such as heart rhythm disturbances, kidney and lung function disorders, stroke, and sudden death. Heart failure is a condition when the heart pump weakens so that it is unable to circulate sufficient blood throughout the body. This condition is also called congestive heart failure. Until now there is no treatment that can specifically treat heart failure problems. This research is a descriptive study which aims to describe the condition of heart failure. By using classification techniques in data mining on data from patients suffering from heart failure using the Naive Bayes algorithm. By using the Rapid Miner tool, data processing is based on the dataset, using classification techniques and data mining stages to classify data on patients suffering from heart failure. By using the Rapid Miner tool, the data processing that will be used as a data collection in this research is collected into 90% training data and 10% testing data. The research results showed an accuracy rate of 80.00%, precision of 66.67% and recall of 100.00%. Based on the research that has been conducted, it is concluded that classification techniques using the Naive Bayes algorithm can be used to determine the potential for life and death in heart failure sufferers.
Recruitment Classification of Security Unit PT. Satria Kencana Abadi Using Naïve Bayes Method Rilvani, Elkin; Surojudin, Nurhadi; Danny, Muhtajuddin; Yoga Pratama, Evan
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4138

Abstract

To get human resources according to company standards, the problem faced in the company is the difficulty of the selection process with a short time and the complexity of the decision making process resulting in subjective decision making. The purpose of this research is to assist the assessment process in making decisions for determining the selection of security units (SATPAM) to be more targeted so that it can help the company. In this study the data used were 697 data with 558 training data and 139 testing data. This test data was carried out using the Naïve Bayes algorithm method to classify so that it can determine accurate and efficient decision making, using Rapidminer tools which have 82 accuracy, 01%, 81.61% Precision, and 88.75% recall. This shows that the Naïve Bayes algorithm method has a good performance in determining decision making during the selection of security forces (SATPAM) at PT. Satria Kencana Abadi.
Prediction of Employee Assessments for Contract Extensions at PT Sagateknindo Sejati Using the Naïve Bayes Algorithm Naya, Candra; Siswandi, Arif; Butsianto, Sufajar; Febriyanti, Febriyanti
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4170

Abstract

Companies must be selective in conducting employee assessments in order to retain employees with the best performance. When assessing employee performance, it is seen from their perseverance and discipline. However, in reality, good employee performance sometimes gets bad reviews and even gets reprimanded by their superiors. This is caused by the employee assessment monitoring system used, namely only personal assessment without using an assessment system and the data collected is less than optimal. This research uses the Naive Bayes method to process data using a data mining algorithm to obtain predictions that can be used as additional references in making employee performance assessment decisions. Aims to predict employee assessments of contract extensions at PT Sagateknindo Sejati. This research is important because it helps in making more accurate decisions regarding employee contract extensions based on existing historical data. Naive Bayes is a data processing algorithm that is classified as a calculation that is easy to understand but its accuracy results are reliable. It is used because it is efficient in managing data with various attributes and is able to produce predictions based on the probability of each existing attribute. The data used in this research includes various variables, using the Rapidminer supporting application to test the accuracy of the system created. Testing was carried out by preparing 320 data and testing 50 randomly selected data. Test data will be analyzed using the Rapidminer supporting application. The test results produced an accuracy of 83.96%.
Application of the C 4.5 Algorithm to Classify Customer Characteristics at PT. Bayer Indonesia Siswandi, Arif; Anwar, M. Syaibani; Susilo, Arif; Hasibuan, Sultan
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4174

Abstract

PT. Bayer Indonesia is a company engaged in drug production. In running its business, companies need to know customer characteristics in determining what actions to take next. This research aims to apply the C 4.5 algorithm in classifying customer characteristics at PT. Bayer Indonesia. The C 4.5 algorithm is a decision tree algorithm that is often used in data mining for classification purposes. This research was conducted to make it easier to find out customer characteristics. Starting with collecting data, then selecting the attributes that will be used. Then the data is separated using split data, the initial comparison used is 60% train data and 40% test data. Then training data is carried out using the C4.5 algorithm. Next, the classification results were evaluated using the confusion matrix method. The data used was 200 data with 9 attributes, obtained an accuracy of 86.25%, precision of 86.25% and recall of 54.55%. Then change the data split parameters to 70% : 30%, 80% : 20% and 90% : 10%. The best accuracy is 100%. The research results show that the C 4.5 algorithm has good performance in classifying the characteristics of PT customers. Bayer Indonesia. The resulting model can be used by companies for more effective marketing strategies and personalized customer service.
Association Rule to Increase Sales Using the Apriori Algorithm Method Ermanto, Ermanto; Halim Anshor, Abdul; Arwan Sulaeman, Asep; Winarni, Sri
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4185

Abstract

The Apriori algorithm is a data mining technique used to find relationship patterns between items in a transaction dataset. In this context, the Apriori algorithm will be used to identify products that are often purchased simultaneously by customers. By understanding these purchasing patterns, companies can design more effective marketing strategies, such as strategic product placement, bundling package offers, and special promotions. This research involves several stages, starting from collecting sales transaction data, data preprocessing, applying the Apriori algorithm, to interpreting the results. The transaction data used is taken from the sales database of a retail store during a certain period. After the data is processed, the Apriori algorithm is applied to identify frequent itemsets and form association rules. The results of this research show that there are several significant purchasing patterns, such as a combination of product A and product B which are often purchased together. By applying data mining using the a priori algorithm method, you can find out which products sell the most. From the results of manual calculations it was found that consumers who bought RB 1060 would buy RB 1099 with 81% confidence, whereas using WEKA it was found that consumers who bought RB 1060 would buy RB 1099 with a confidence value of 82%.
Website Development as a Media for Disseminating Information in RT. 39 Jelutung Village, Jambi City Ikhsan, Muhammad; Darmuji, Darmuji; Adhiatma, Novri; Helmina, Helmina
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4198

Abstract

RT 39 Jelutung Village, Jambi City is currently facing challenges in terms of disseminating information to its citizens. Conventional methods that have been used, such as announcements on information boards, regular meetings, and distributing leaflets, are often ineffective in reaching all residents. This research seeks to create a website for sharing information for disseminating information in RT. 39, Jelutung Village, Jambi City, using the Waterfall method and UML modeling to describe the system flow. This website is designed to facilitate effective communication between RT administrators and residents and provide fast and accurate access to information regarding activities, announcements and public services. The Waterfall methodology includes the phases of needs analysis, design, implementation, testing and maintenance. UML modeling is used to visually describe the structure and flow of a system, including use case diagrams, activity diagrams, and class diagrams. The finding is a website with main features such as Information Portal, Website Dashboard, Manage Mail. Evaluation was carried out through a survey of RT residents. 39 to assess the effectiveness and satisfaction with the use of this website. The evaluation results show that this website has succeeded in increasing information accessibility and citizen participation in RT activities. 39. Thus, it is hoped that the development of this website can become a model that can be applied in other RTs To enhance the caliber of communication and informational offerings.
Implementation And Simulation Of Dynamic Arp Inspection In Cisco Packet Tracer For Network Security Putra, Fauzan Prasetyo Eka; Ubaidi, Ubaidi; Tamam, Alief Badrit; Efendi, Reynal Widya
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4199

Abstract

Dynamic ARP Inspection (DAI) is a security feature that helps prevent Address Resolution Protocol (ARP) spoofing attacks, which can compromise the integrity and confidentiality of data in a network. This paper presents the implementation and simulation of DAI using Cisco Packet Tracer, a network simulation tool. The goal is to show how DAI can be configured and used to improve network security by verifying ARP packets in a switched network. The implementation involved setting up a network topology with switches and end devices, configuring DHCP snooping, and enabling DAI on the switches. Simulations tested various scenarios, including normal operation, ARP spoofing attacks, and the network response to these attacks with DAI enabled. The results show that DAI effectively mitigates ARP spoofing attacks, ensuring only legitimate ARP packets are forwarded, thus protecting the network from potential security breaches. The study concludes that implementing DAI is an important step in securing networks, especially in environments with sensitive data and high security needs. This paper serves as a practical guide for network administrators looking to improve their network security posture using Cisco Packet Tracer.
Systematic Literature Review: Security Gap Detection On Websites Using Owasp Zap Putra, Fauzan Prasetyo Eka; Ubaidi, Ubaidi; Hamzah, Amir; Pramadi, Walid Agel; Nuraini, Alief
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4227

Abstract

This research highlights the detection of security vulnerabilities on websites using OWASP ZAP, a highly regarded open-source web security testing tool. Through a comprehensive literature review approach and systematic research methodology, this research emphasizes the urgency of addressing the ever-evolving security threats in web systems. Web security is a crucial aspect of information technology as more and more sensitive data is transmitted through web applications. OWASP ZAP, recognized for its reliability in identifying various security holes, was used to evaluate its effectiveness and efficiency in detecting vulnerabilities in web applications. This tool assists developers and security researchers in finding and fixing weaknesses that could be exploited by attackers. The results of the study show that OWASP ZAP is not only effective in identifying vulnerabilities such as SQL Injection, XSS (Cross-Site Scripting), and misconfiguration but also provides practical solutions to strengthen overall web security. Additionally, this research identifies several challenges faced when using OWASP ZAP and offers recommendations to address these issues. This study makes a significant contribution towards a better understanding of web security and offers recommendations for the implementation of better security testing tools in web development environments. Consequently, this research encourages the adoption of more proactive and systematic security practices in web application development.
Application of Internet of Things Technology in Monitoring Water Quality in Fishponds Putra , Fauzan Prasetyo Eka; Ubaidi, Ubaidi; Saputra, Rama Nurja; Haris, Farras Maulana; Barokah, Selviana Nur Rizqi
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4231

Abstract

Monitoring water quality in fish ponds is essential to maintain fish health and productivity. In recent years, Internet of Things (IoT) technology has emerged as an effective solution to efficiently monitor and manage water quality. This article describes the application of IoT technology in monitoring the water quality of aquaculture ponds with the aim of improving fish productivity and welfare. Data on water quality parameters such as temperature, dissolved oxygen, pH, ammonia levels, and turbidity can be monitored in real time through a network of connected sensors. The main advantage of using IoT for fish pond water quality monitoring is that it can provide accurate and fast information on water conditions. This allows fish farmers to proactively take appropriate actions to maintain an optimal water environment for fish growth. In addition, integration with data management and remote monitoring systems allows fish pond owners to continuously monitor water conditions, even in remote locations. This helps reduce the risk of environmental damage and fish loss due to inappropriate water quality conditions. Therefore, the use of IoT technology for water quality monitoring in fish ponds not only increases productivity but also ensures the well-being of fish and reduces negative impacts on the environment.
The Sentiment Analysis of Bekasi Floods Using SVM and Naive Bayes with Advanced Feature Selection Amali, Amali; Maulana, Donny; Widodo, Edy; Firmansyah, Andri; Danny, Muhtajuddin
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4268

Abstract

Flood management in Bekasi City poses significant challenges, necessitating strategies grounded in an understanding of community sentiment. This study aims to develop and optimize sentiment analysis of social media data related to flooding using Support Vector Machine (SVM) and advanced feature selection techniques. The primary goal is to enhance the accuracy of classifying public sentiment toward flood management efforts in Bekasi City. Data is collected from various social media platforms, preprocessed, and analyzed using SVM with feature selection techniques like Information Gain and Analysis of Variance (ANOVA). (Thoriq et al., 2023) Our findings indicate that using SVM with advanced feature selection significantly improves sentiment classification accuracy compared to standard methods. These results offer insights into public perceptions, helping policymakers improve management strategies and communication for flood events. This method assists in understanding community responses and pinpointing critical areas needing attention. Moreover, this study contributes to disaster management in urban flood-prone areas by presenting a methodological approach applicable to other disaster contexts. Integrating social media sentiment analysis with advanced machine learning techniques offers a robust framework for real-time public sentiment assessment, enhancing disaster response strategies. Furthermore, these techniques help create a more resilient urban environment by improving the efficiency and effectiveness of flood management practices. This comprehensive tool is essential for better preparedness, response, and recovery from flood events, ultimately enhancing community resilience and safety in Bekasi City. This research is part of machine learning in disaster management and a valuable asset for city planners and disaster professionals around the world.

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