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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 Articles sent to the Brilliance may not be published elsewhere.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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Analysis of Hybrid Learning Model Interest Selection for students using the Multi-Attribute Utility Theory Method Case Study: Mulia University Alimyaningtias, Wahyu Nur; Saputra, Risdin; Prayogo, Ari; Sudinugraha, Tri
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.v3i2.3069

Abstract

Hybrid learning is a learning method that combines or combines online learning with face-to-face learning (PTM). so that in its implementation, there are times when students and teaching staff meet face to face in class. However, in its implementation there are several obstacles, one of which is the number of students attending class and decreasing interest in learning, resulting in a poor final semester assessment. In connection with the problems faced, this research will try to measure interest in the hybrid learning model that has been implemented at Mulia University using the Multi-Attribute Utility Theory method, where this method is a decision-making method used to evaluate alternatives by considering several attributes. relevant and selecting the alternative that best meets the needs and preferences of the decision maker.
ASC Timetables for Setting Subjects in Integrated Islamic Elementary School; Intelligent Planning for Learning Systems Yahya; Fitrina Defi, Wahyu; Febrina, Weli
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.v3i2.3082

Abstract

This study investigates the implementation and effectiveness of ASC Timetables, an intelligent planning system, in setting subjects for an Integrated Islamic Elementary School at Riyadhoturrohman Panyabungan. The research explores the complexities of scheduling subjects within the context of Islamic education and analyzes the intelligent planning mechanisms integrated into ASC Timetables. A qualitative research approach, employing in-depth interviews, observations, and document analysis, was utilized to gather data from teachers, school administrators, and staff directly involved in using ASC Timetables.The findings of this study reveal the intricate process of subject scheduling in an integrated Islamic elementary school and highlight the significance of intelligent planning systems in managing diverse subjects while adhering to Islamic educational principles. The research illuminates the challenges faced by educators and administrators and discusses the solutions provided by ASC Timetables. Moreover, it identifies the system’s ability to adapt and optimize schedules, ensuring an effective and balanced allocation of learning hours for various subjects. This research contributes to the understanding of how intelligent planning systems like ASC Timetables enhance the efficiency of subject scheduling in a unique educational context. The insights gained from this study can inform educational practitioners, policymakers, and system developers, guiding the integration of intelligent planning tools in diverse educational environments, especially those with specific cultural and religious requirements.
Optimization of the Shortest Tsunami Evacuation Route Using Djikstra’s Algorithm in Benoa Village K, Ida Bagus Kade Puja Arimbawa; Sukartiasih, Wayan; Sedayu, Agung
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.v3i2.3089

Abstract

Benoa Village has an area of approximately 2.38 km² and a population of 9,569 people in 2020 with a population density of 4,013 people/km2. This area is included in the list of tsunami-prone areas because the area is located on the edge of the Indian Ocean, which is known as an area with a high level of earthquake and volcanic activity. The 2004 tsunami that hit the coast of the Indian Ocean increased the potential for similar disasters to occur in the area. Determination of the shortest evacuation route in Benoa Village using Djikstra Algorithm. The result obtained is a path from the evacuation starting point vertex to the comfort zone node. Thisvertex represents places and road intersections arranged in the form of a weighted graph (distance) with a total of 51 vertexs, and an Adjacency Matrix is formed which is processed using the C++ Program. The Safe Zone vertex (Grand Hyatt Bali Temporary Meet Point (V50), Hattrick Futsal (V51)) are headed from the evacuation starting point of Serangan Beach (4.49km to V50, 6.94km to V51), Noanui Beach (3.95km to V50, 6.39km to V51), Samuh Beach (2.75km to V50, 5, 19km to V51), Nusa Dua Beach A (3.54km to V50, 5.48km to V51), Nusa Dua Beach B (3.47km to V50, 4.79km to V51), Peninsula Island (4.69km to V50, 4.80km to V51), Megiat Beach (4.81km to V50, 4.15km to V51) and Geger Beach (5.86km to V50, 5.09km to V51).
Development of an Intelligent System to Determine Land Suitability for Horticultural Crops on Vegetable Commodities Sahputra, Ilham; Usnawiah; Fhonna, Rizky Putra; Siregar, Dinda Saima Agustina; Angelina, Difa
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.v3i2.3100

Abstract

Global climate change has a significant impact on the agricultural sector, including horticulture, with climate fluctuations such as increased temperatures and changes in rainfall patterns potentially affecting crop productivity. Sustainable horticultural agriculture is important for safeguarding natural resources and reducing environmental impacts. However, challenges from climate change and variations in land conditions can affect horticultural crop production. Identifying crops that are suitable for the climate and land conditions is key to agricultural sustainability. An intelligent and adaptive approach is needed in selecting the right crops to grow in the face of climate change. This research develops an artificial intelligence application for the recommendation of horticultural crop types according to land conditions and climate change. The model built involves AHP and MFEP methods. The model takes into account various land parameters with weights determined through the AHP approach, allowing this AI application to provide accurate recommendations based on data and modeling. Based on the tests conducted, the system was able to produce analysis with an accuracy rate of 85%.
Heart Rate Monitoring, Blood Oxygen Levels and Location Determination for Covid 19 Patients Using Internet of Things Technology Sipahutar, Erwinsyah; Oktrison; Budiansyah, Arie; Candra, Rudi Arif; Ilham, Dirja Nur
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.v3i2.3105

Abstract

The COVID-19 virus is very dangerous. In addition to attacking the lungs, this virus can also attack the heart directly. The relationship between heart health and COVID-19 occurs because blood vessel clots in COVID-19 patients increase the risk of blood vessel disorders and make the heart work harder. Therefore, researchers designed a heart rate monitoring device. This tool serves to maintain heart health and oxygen levels in the patient's blood and can also monitor people under COVID surveillance. The formulation of the problem from this research is how to design an IoT system to monitor COVID-19 patients through heart rate and location and how it performs. The purpose of this study is to analyze the performance of the device and design a tool to measure heart rate and blood oxygen levels through the IoT system and GPS location so that doctors can access heart rate data from any location. This research method uses a Gy-MAX30100 sensor, Wemos, and a GPS module. Of the 2 data samples that have been tested, the highest heart rate value is 78 bpm, the lowest is 76 bpm, and the highest oxygen level is 95 mmHg and the lowest is 93 mmHg. So in conclusion, this tool can make it easier for doctors to get important information about the condition of patients or people under observation that can be accessed by doctors anywhere and anytime via the internet.
4G LTE Network Quality and TCP/IP Performance of Telkomsel Provider in the Lolong Belanti Area Yuhanef, Afrizal; Aulia, Siska; Pratama, Ilham Aditya; Setiawan, Herry
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.v3i2.3120

Abstract

Lolong Belanti is one of the sub-districts in North Padang District, Padang City, West Sumatra Province. In Lolong Belanti cellular telecommunications is growing very rapidly and has become an important need for the community. Therefore, research needs to be done so that it can be a reference for providers to improve the quality and quantity of 4G LTE networks in Lolong Belanti. The measurement results on weekdays obtained the percentage of good to excellent 4G network categories have a percentage value of RSRP 99.04%, SINR 12.14% and Throughput 98.51% and the average value of download bandwidth 28.26 Mbps, upload bandwidth 23.92 Mbps, throughput 1,282,003 kbps, packet loss 0.103%, delay 5.92 ms and jitter 5.97%. While on holidays, the percentage of 4G network categories is good to very good having a percentage value of 97.91% RSRP, 12.28 SINR and 98.51% Throughput and has an average value of 13.68 Mbps download bandwidth, 17.94 Mbps upload bandwidth, 647.688 kbps Throughput, 0.26% packet loss, 17.428 ms dellay and 17.566 ms jitter.  The quality of Telkomsel's 4G LTE network provider in Lolong Belanti Village on holidays is better than on weekdays. And the comparison of TCP/IP performance for Bandwidth Download, Bandwidth Upload, Throughput and Packet Loss is superior on weekdays compared to holidays, and for Delay and Jitter is better on holidays than on weekdays.
Implementation of Random Shuffle Algorithm to Randomize Questions in Anti-Corruption Prevention Game Askar; Pasnur; Asrul; Amiruddin, Amran; Resha, Muhammad; Wijaya T, Andri
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.v3i2.3126

Abstract

Socializing anti-corruption education in Indonesia is crucial to fostering public aversion towards corruption. Games are a highly effective medium for disseminating anti-corruption prevention during school years. This research aims to implement anti-corruption education within a game using the shuffle random algorithm to ensure the successful randomization of anti-corruption questions. This data was obtained through a literature review. The method used in this study is the Multimedia Development Life Cycle (MDLC). The results of this research indicate that the testing yielded a 100% accuracy rate for the shuffle random algorithm in randomizing the questions. The accuracy rate demonstrates that the shuffle random algorithm can effectively randomize the questions and is easy to implement.
The Effect of Chatbot Services on Online Shop Customer Satisfaction: The Effect of Chatbot Services on Online Shop Customer Satisfaction Kappi, Cecep M Kappi; Marlina, Lina
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.v3i2.3133

Abstract

The increasing trend of e-commerce users has not been matched by customer satisfaction in the shopping process. Indonesia has the highest level of dissatisfaction compared to other ASEAN countries. Although chatbot technology has been used as an aid to optimize services, dissatisfaction still occurs with regard to agility, service assurance, reliability, scalability and security. The purpose of this study is to determine chatbot services in providing customer satisfaction. The research approach uses quantitative with explantory survey method. The research population is online shop users using rondom sampling, 175 respondents were collected. Assisted by PLS SEM analysis tool. The results show that chatbot social orientation services contribute to online shop customer satisfaction. Likewise, chatbot personification makes a positive contribution to online shop customer satisfaction.
Application of the Learning Vector Quantization Algorithm for Classification of Students with the Potential to Drop Out Widiantara, I Gusti Made Wahyu Krisna; Aryanto, Kadek Yota Ernanda; Sunarya, I Made Gede
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.v3i2.3155

Abstract

Universities, as providers of academic education services, are required to provide an optimal educational process for students to produce a generation of quality human beings. Student learning success is seen as the success of universities in implementing the higher education process. One of the problems universities face in maintaining the quality of education is student dropout. The high dropout rate in universities can impact accreditation assessments. As a result, it will affect the level of public trust. The number of dropouts in higher education can be minimized from an early age by analyzing the factors that cause student dropouts using data on students who graduated and those who dropped out. This data can be used to determine student dropout patterns by classifying them using the artificial neural network learning vector quantization (LVQ) approach. The data used in this research was 4053, consisting of 3840 graduate student data and 213 dropout student data. This data is considered unbalanced, an unbalanced dataset can cause errors because the model tends to classify the majority class with a high classification and pays less attention to the minority class. So, it is necessary to apply oversampling techniques to overcome this problem. The research results show that the application of the LVQ method to unbalanced data produces an accuracy value of 95.53%, a precision value of 100%, a recall value of 15.02% and an f1-score of 0.26, while the application of the LVQ method to data that has undergone resampling resulting in an accuracy value of 94.66%, a precision value of 92.22%, a recall value of 97.55%, and an f1-score value of 0.95. The LVQ method can be used to classify dropout students with excellent results.
Air Pollution Standard Index (APSI) Detection Application Based on the Flask Model Mahalisa, Galih; Nurarminarahmah
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.v3i2.3194

Abstract

Air pollution is a global environmental problem that threatens human health and ecosystems. The Air Pollution Standards Index (APSI) is an important metric for measuring air quality and informing the public about the pollution level in an area. In the digital era, web-based applications have become an effective tool for providing real-time APSI information to the public. This research introduces an Air Pollution Standard Index (APSI) detection application based on the Flask model using the SVM (Support Vector Machine) algorithm to predict APSI. This application collects air quality data from various sensors distributed throughout the region and uses SVM (Support Vector Machine) to process the data. APSI prediction results are then presented to users via an easy-to-use web interface. The main advantage of this application is its ability to provide real-time APSI information so that users can take appropriate action according to the level of air pollution in their area. This application can help the public and environmental authorities proactively deal with air pollution and protect human and environmental health. APSI Prediction Accuracy: Through SVM model training, this application can predict the Air Pollution Standard Index (APSI) with sufficient accuracy. While there is potential to improve accuracy through more data collection and model updates, initial results are promising

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