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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 Articles sent to the Brilliance may not be published elsewhere.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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IoT-Based Rainfall Monitoring System for Chili Farming Land Rahmadani Putri; Ratna Dewi; Silfia Rifka; Sri Nita; Andi Ahmad Dahlan
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.
Application of Dempster-Shafer Theory Method in Expert System for Diagnosis of Psychological Disorders in Children Azhari, M. Faishal; Putri, Raissa Amanda
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.v4i1.3764

Abstract

The development of technology at this time has changed very rapidly in recent decades. Expert system is one of the sub-sections of artificial intelligence that aims to process and display the results of the conclusion of the previous process based on the knowledge that has been obtained. The purpose of this research is to apply the Dempster-Shafer method to the expert system for diagnosing psychological disorders in children and to design and build an expert system using the Dempster-Shafer method on a website-based expert system for diagnosing psychological disorders in children. Based on the results and discussion in this study, it can be concluded that the Dempster Shafer Method can be applied properly in an expert system for diagnosing psychological disorders in children. The Dempster Shafer method in this system gets results that are accurate enough so that it can generate a diagnosis based on symptoms accompanied by a handling solution. This expert system application for diagnosing psychological disorders in children can diagnose and determine the diagnosis results from consultations conducted by users using the Dempster Shafer method. Where in the example case, the percentage result is around 87.15%. This system can also help parents to get information about children's psychological disorders, symptoms, and solutions that can be applied.
Teaching Product Design Through The CANVA Application To Enhance The Brand Image Of The Company Orisa, Mira; Faisol, Ahmad; Ashari, Muhammad Ibrahin
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.v4i1.3775

Abstract

The study aims to investigate the effectiveness of using Canva applications to improve employees' graphic design skills. The research was conducted by observing a company that operates in the areas of Maintenance service, General supply and renovation. This research activity is designed into several stages such as the partner observation phase, the preparatory stage of training, the training stage, and the final stage of evaluation. Based on observations stated that the company has employees from various disciplines such as electro, civil, electric energy. We provide training to employees in graphic design. For us this knowledge is also very important to the employees to help companies improve their brand image. In the business world, one of the most crucial marketing tools for presenting and promoting a product to potential customer is an engaging and educational product design presentation. The purpose of this study is to determine how simple it is to create brand image successful like a poster, flyers, logo, and brochure using Canva's graphic design platform. Users can easily design and edit with Canva's array of design tools, which include text tools, color adjustment tools, image manipulation tools, grid tools, and more. They can design their brand image easily using online applications like canva. canva provides easy access anywhere and anytime because it is internet-based.
Comparative Analysis Of Machine Learning Models For Greenhouse Microclimate Prediction Cletus, Felicia; John, Anagu Emmanuel
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.v4i1.3783

Abstract

The research assesses the effectiveness of these models as Bi-LSTM, ANN, GBM, and RF in predicting microclimate factors like temperature, humidity, and CO2 levels. It also highlights the constraints associated with employing machine learning models for greenhouse microclimate prediction and suggests potential areas for future investigation. The findings indicate that both ensemble techniques (Gradient Boosting Machine and Random Forest) and deep learning frameworks (ANN and BI-LSTM) performed well during the assessment. While both ensemble methods exhibited impressive results, Gradient Boosting Machine (GBM) slightly surpassed Random Forest (RF) across various evaluation criteria. GBM attained a notable R-squared value of 0.9998, signifying its robust fit and capacity to elucidate data variability, in addition to a Root Mean Squared Error (RMSE) of 0.0079 and Mean Absolute Error (MAE) of 0.0001. RF demonstrated similar outcomes, with an R-squared value of 0.9999. Conversely, ANN outperformed BI-LSTM in terms of R-squared values and MAE, displaying an R-squared value of 0.999999 and a MAE of 0.0079. An analysis of the sensitivity of the ANN model revealed that altering the average indoor relative humidity in the first sensor had the greatest impact on the prediction outcome among other variables. Assessing and ranking the importance of each feature used in training the RF and GBM models indicated that the average relative humidity in the second sensor held the highest significance, with any modification to it likely to notably influence the prediction outcome. These results support the notion that machine learning algorithms serve as effective predictive tools, offering valuable insights for enhancing greenhouse operations. Future research should focus on practical implications and real-world applications, particularly in optimizing hyperparameters.

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