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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 Articles sent to the Brilliance may not be published elsewhere.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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Web-Based Design Of Vehicle Management Information System Of Widyatama University Wahyu Wibowo, Ari Purno; Heryono, Heri; Hamdani, Dani
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.v4i1.3641

Abstract

Vehicle inventory management and data collection information system is a system based on a Management Information System so that it has complete implementation functions that aim to record all vehicles. The main objective of developing this information system is to provide services that are faster, transparent and accurate on the service side of borrowing official vehicles within the Widyatama University environment. In the current vehicle inventory information system, it is not yet computerized and is done manually. The inventory registration information system is expected to be useful for minimizing errors that have occurred so far such as the unclear condition of the vehicle and when the vehicle will return, the design of the inventory information system is expected to produce an inventory information system that can work optimally and efficiently can perform testing services, readiness of drivers and management of motorized vehicles that operate according to the provisions that have been regulated, where the type of service can be adjusted to vehicle test data that is periodic, mutations in (Arrive) / out (booking) can be seen clearly by officers or users, other functions are there are changes to the function and status of the vehicle so that the vehicle will always be available and ready to be used.
Text as a Social Network Analysis Topography And Political Communication In Indonesia Heryono, Heri Heryono; Hamdani, Dani; Purno Wahyu, Ari
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.v4i1.3645

Abstract

Political communication has a central role in shaping public opinion and political dynamics in Indonesia. This research aims to investigate the role of text in the context of Social Network Analysis (SNA) and political communication in Indonesia. This research combines NLP (Natural Language Processing) text analysis with SNA to understand how texts related to politics can provide insight into the relationship between political figures, political issues, and society. The research methodology involves collecting text data from various sources, such as social media, online news, blogs, and political discussion forums. The text data is then processed, analyzed and modeled with SNA analysis tools and NLP algorithms to identify relationships and communication patterns in a political context. In addition, this research also considers how the sentiments in these texts can influence the dynamics of sociopolitical networks. It is hoped that the results of this research will provide a deeper understanding of how political texts can be used as a tool for SNA analysis, with a focus on the Indonesian context. The findings of this research can be useful for political researchers, communication practitioners, and political decision makers to understand the political dynamics that are developing in the digital era. Apart from that, this research also has implications in understanding how political issues and political figures are understood and perceived by the public in political communication in Indonesia.
Digital Marketing Efforts to Improve Products of Micro Small and Medium Enterprises (UMKM) in Tegal Santoso, Nugroho Adhi; Nugroho, Bangkit Indarmawan; Murtopo, Aang Alim; Surorejo, Sarif; Gunawan, Gunawan
Brilliance: Research of Artificial Intelligence Vol. 3 No. 2 (2023): Brilliance: Research of Artificial Intelligence, Article Research November 2023
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v3i2.3646

Abstract

Digital marketing is promotional activities and market search through the media digitally online by utilizing various means such as social networks. The aim of this research is to increase knowledge and skills about digital marketing, especially social media, for Small and Medium Enterprises (SME) business people to increase their sales and profits. Digital marketing is the use of social media networks to carry out promotional activities and map digital markets. By using computers or other electronic equipment, digital marketing ideas can bring together geographically diverse parties. The aim of this research is to identify the most effective digital marketing tactics for the growth of MSMEs in Tegal City and Tegal Regency. The method used in this research is descriptive qualitative. With Data collection through observation, interviews, and secondary sources, such as books, journals, and articles, were used to collect information for this research. The results of this research show that the productivity growth of MSMEs in Tegal City and Tegal Regency has not been positive. Even when a website for an online business has been created, not everyone has implemented a digital marketing plan. It can be seen that digital marketing strategies have not received much attention from MSMEs in Tegal City and Tegal Regency. So it is hoped that MSMEs in Tegal City and Tegal Regency can adapt to changing times, namely selling online using digital marketing strategies.
IoT-Based Rainfall Monitoring System for Chili Farming Land Putri, Rahmadani; Dewi, Ratna; Rifka, Silfia; Nita, Sri; Dahlan, Andi Ahmad
Brilliance: Research of Artificial Intelligence Vol. 3 No. 2 (2023): Brilliance: Research of Artificial Intelligence, Article Research November 2023
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v3i2.3649

Abstract

This research focuses on the design and implementation of a rainfall monitoring system for chili pepper farms using Internet of Things (IoT) technology. The rainfall monitoring system consists of a transmitter system, a receiver system, the Thingspeak platform as a database, and a weather station application that can be accessed via a mobile device. The weather station application is built using the MIT App Inventor platform. In the testing phase, the system successfully collected data from two sensors used, namely the rainfall intensity sensor and the raindrop sensor. The test results showed that the data obtained from the rainfall intensity sensor was 0.25 inches and the raindrop sensor was 1. This result shows that there was no rain during the test. This rain intensity and raindrop data can provide farmers with an overview of the weather conditions in the chili pepper farm. So, with this rainfall monitoring system, farmers can monitor the condition of their agricultural land in real-time. The collected data can help farmers to care for chili pepper plants more effectively and adapt to environmental changes. In addition, this system is expected to increase the productivity of chili pepper farming because it uses a more precise and responsive approach to changes in environmental conditions on the chili pepper farm.
Comparative Analysis of Machine Learning Models for Real-Time Disaster Tweet Classification: Enhancing Emergency Response with Social Media Analytics Airlangga, Gregorius
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.v4i1.3669

Abstract

In the realm of disaster management, the real-time analysis of social media data, particularly from Twitter, has become indispensable. This study investigates the efficacy of various machine learning models in classifying tweets pertaining to disaster scenarios, with the goal of bolstering emergency response systems. A dataset of tweets, categorized as related or unrelated to disasters, underwent a rigorous preprocessing regimen to facilitate the evaluation of five distinct machine learning models: Naïve Bayes, Random Forest, Logistic Regression, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks. The performance of these models was assessed based on accuracy, precision, recall, and F1 score. The results indicated that the SVM model excelled, achieving an accuracy of 89%, precision of 88%, recall of 89%, and an F1 score of 88%, making it the most robust for text classification tasks within the context of disaster-related data. The LSTM model also performed notably well, with an accuracy of 87%, precision of 86%, recall of 87%, and F1 score of 86%, underscoring the potential of deep learning models in processing sequential data. In comparison, Naïve Bayes, Random Forest, and Logistic Regression models demonstrated moderate performance, with accuracy and F1 scores in the range of 76-77% and 72-73%, respectively. These insights are crucial for the development of advanced social media monitoring tools that can significantly enhance the timeliness and precision of crisis response. The research not only highlights the necessity of selecting appropriate machine learning models for specific NLP tasks but also sets the stage for future investigations into the integration of hybrid analytical frameworks. This study establishes a foundation for leveraging machine learning to transform social media data into actionable intelligence, thereby contributing to more effective disaster management and community safety strategies.
Comparative Analysis of Machine Learning Algorithms for Multi-Class Tree Species Classification Using Airborne LiDAR Data Airlangga, Gregorius
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.v4i1.3673

Abstract

Forests hold vital ecological significance, and the ability to accurately classify tree species is integral to conservation and management practices. This research investigates the application of machine learning techniques to airborne Light Detection and Ranging (LiDAR) data for the multi-class classification of tree species, specifically Alder, Aspen, Birch, Fir, Pine, Spruce, and Tilia. High-density LiDAR data from varied forest landscapes were subjected to a rigorous preprocessing and noise reduction protocol, followed by feature extraction to discern structural characteristics indicative of species identity. We assessed the performance of six machine learning models: Logistic Regression, Decision Tree, Random Forest, Support Vector Classifier (SVC), k-Nearest Neighbors (KNN), and Gradient Boosting. The analysis was based on metrics of accuracy, precision, recall, and F1 score. Logistic Regression and Random Forest models outperformed others, achieving accuracies of 0.81, precision of 0.80, recall of 0.81, and an F1 score of 0.80. In contrast, the KNN algorithm had the lowest accuracy of 0.60, precision and recall of 0.60, and an F1 score of 0.59. These results demonstrate the robustness of Logistic Regression and Random Forest for classifying complex LiDAR datasets. The study underscores the potential of these models to support ecological monitoring, enhance forest management, and aid in biodiversity conservation. Future research directions include the fusion of LiDAR data with other environmental variables, application of deep learning for improved feature extraction, and validation of the models across broader species and geographical ranges. This research marks a significant step towards leveraging advanced machine learning to interpret and utilize LiDAR data for environmental and ecological applications.
Factors related to students' learning motivation at the Aceh Ministry of Health Polytechnic, Tapaktuan Nursing Study Program, South Aceh Regency Rahmi, Cut; Rasima, Rasima; Lizam, T. Cut; Syamirwan, Syamirwan; Susanti, Susanti
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.v4i1.3684

Abstract

Learning motivation is an impulse that arises both from within and outside the student, which can generate enthusiasm and enthusiasm for learning and provide direction to learning activities so that the desired goals can be achieved. Educators need to understand the importance of the role of motivation in the learning process so that they can carry out various forms of action or assistance to students. This study aims to identify factors related to learning motivation among students at the Aceh Ministry of Health Polytechnic, Tapaktuan nursing study program, South Aceh Regency. This type of research is descriptive correlative research with a cross-sectional study approach. The study used total sampling to select the research sample. The subjects of this research were 172 students at level I, level II, and level III of the Aceh Ministry of Health Polytechnic, Tapaktuan Nursing Study Program, South Aceh Regency. The data collection technique in the research used a questionnaire and data analysis was carried out using univariate and bivariate analysis with the chi-square statistical test. The results were obtained namely, there is a relationship between interest factors (p-value 0.005), family environment (p-value 0.002), and school environment (p-value 0.005) with student learning motivation and there is no relationship between expectation factors and student learning motivation at the Aceh Ministry of Health Polytechnic, Tapaktuan Nursing Prode, South Aceh Regency with a P value of 0.018.
Design Environmentally-Friendly Incinerator and Hybrid Smokeless Incinerator Sorong of Merchant Marine Polytechnic Riyanto, Budi; Widarbowo, Dodik; Idris, Muh; Nugroho, Danang D.S.; Setiyono, Muji
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.v4i1.3703

Abstract

The problem of waste is the subject of discussion from time to time, waste that is not managed properly has a negative impact on the environment. There needs to be a waste management system so that waste problems can be suppressed and overcome, the most effective method of overcoming waste is burning but the results of burning will cause pollution that can pollute the air and can affect the environment. In an effort to overcome the problem of environmentally friendly waste, the Sorong of Merchant Marine Polytechnic designed a waste handling system through the Design of Environmentally-Friendly and Hybrid smokeless incinerators whose work system uses smokeless combustion using “hybrid power” sources, solar cell and power plant company. This research uses qualitative methods referring to previous research. The Incinerator working system is to process waste in an environmentally friendly process using the automatic combustion method to turn waste into residue through several levels of filtering in the “incinerator chamber”. The combustion of waste will also cause smoke and gas which will be flowed by the "blower" then suppressed and eliminated using the "smoke and gas remover" system by isolating it in a room with a spray and sprinkle device that is driven by high-pressure water power from the "water pump". There are two filtration systems in this incinerator system, first "gas filtration" which is used to capture and trap harmful gases, second water filtration is used to filter waste water (aerosols) from the smoke and gas remover process, clean water filtering results will be accommodated and recirculated to the "smoke and gas remover" using a "water pump". The "Hybrid power" source in this tool is used to drive the "conveyor", "automatic waste door", "automatic lighter", "blower" and "water pump".
Monitoring cattle farms using Cloud Computing-based Internet of Things (IOT) tools using Artificial Intelligence Methods Rahayu, Ria Sri; Wahyu Wibowo, Ari Purno
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.v4i1.3736

Abstract

Cows are animals  valuable commodity  and it one of the economic supports for people in animal husbandry and agriculture, cows it selves able to  used for meat, there are currently many cattle farms in Indonesia and spread across several regions, the cattle breeding or livestock proses  Currently including in two types,   farming in cages and farming outside cages, the cows themselves can easily be infected by diseases which spread quickly to other cows, large numbers of cows diasble to monitor because of the limited equipment and number of farmers, the number of cages is flat being far from settlement areas will make the selection process difficult and disable simultaneously. To handle this problem, it can be deal with using sensor devices that are configured with IoT devices. These devices easily monitored health and room temperature which can be used for 24 hours, the results of the data from the temperature sensor are displayed information that represent like dashboard and displays the cow's temperature data in graphical view. The system sets a temperature range of 38.6 - 38.9. If above this temperature the cow is in distemper condition and needs to be quarantined and won’t spread to another cow. This system provide information and make it easier for farmers to supervise their livestock.
Implementation of Web Based Leave Information System at PT Arutmin Indonesia Tambang Kintap Maulana, Dhiya Ulhaq; Supriyanto, Arif; Utomo, Hendrik Setyo; Rahmanto, Oky
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.v4i1.3754

Abstract

Leave is one of the rights that must be given to employees by a company. The leave application process at PT Arutmin Indonesia Tambang Kintap is still done manually, starting from the leave application to the results of the leave decision. The process of checking employee leave balances, leave applications, approvals and leave reports still relies on previous leave files. This kind of management process is often complained about because it is felt to be less effective and efficient when searching, changing, deleting data and data redundancy often occurs. Therefore, the aim of this research is to build and implement an employee leave information system which is expected to be able to help the process of managing leave in the Company. This information system was designed using ERD, DFD using the waterfall system development model. This system was built based on a website using the My database. SQL Based on the results of system functionality testing, this leave information system can function well without any problems.

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