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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 Articles sent to the Brilliance may not be published elsewhere.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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Technology Readiness of Retail MSMEs for Digital Sales: Evidence from Kramat, Tegal Surorejo, Sarif; Santoso, Nugroho Adhi; Cahyati , Divia Faiqotul
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6879

Abstract

Digital transformation has become a key driver for enhancing the competitiveness and sustainability of Micro, Small, and Medium Enterprises (MSMEs). This study aims to analyze the digitalization readiness of retail MSMEs in Kramat District, Tegal Regency, by applying the Technology Readiness Index (TRI) model consisting of four dimensions: optimism, innovativeness, discomfort, and insecurity. A quantitative survey method was employed involving 70 MSME respondents, and data were analyzed using descriptive statistics. The findings reveal that optimism (3.45), discomfort (3.37), and insecurity (3.32) fall into the high category, while innovativeness (2.75) is in the medium category. Item-level analysis shows that Q4 (optimism) recorded the highest score (3.59), reflecting a strong belief in the benefits of technology, whereas Q7 (innovativeness) obtained the lowest (2.26), indicating low initiative in adopting new digital tools. These results suggest that retail MSMEs in Kramat District have a positive perception of technology but remain cautious in terms of proactive innovation. The study concludes that enhancing innovativeness through digital skills training, mentoring, and policy support is essential for accelerating MSME digital transformation. This research contributes to the literature by providing empirical evidence from a semi-urban context in Indonesia and offers practical recommendations for policymakers and stakeholders in strengthening MSME competitiveness in the digital era.
Development Of A Solar Powered IoT Based Landslide Detection System Dwiyanto, Dwiyanto; Hidayat, Iman; Alamsyah, Rizky; Sri Lestari, Ninik; Ramadi, Givy Devira; Sukirno, Sukirno
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6887

Abstract

The surfaces of three major tectonic plates the Eurasian Plate, the Indo Australian Plate, and the Pacific Plate intersect in Indonesia. This condition creates a high risk of earthquakes and landslides in areas located at these plate boundaries. The urgency of this research lies in developing a landslide detection device to help communities living in landslide-prone areas remain alert to disasters that may occur at any time. The objective of this study is to develop an Internet of Things (IoT)-based landslide detection device powered by solar energy. This device does not rely on external power sources but utilizes solar energy as its primary power supply. The research employs an experimental method. The steps include problem identification, literature review, system development methodology, design and application development, testing, and analysis of test results.Landslide detection is carried out using vibration sensors to detect ground tremors that may indicate a landslide, tilt sensors to monitor changes in ground inclination, and soil moisture sensors to measure soil humidity. An Arduino microcontroller processes data from the sensors and transmits signals to the warning system, while solar panels generate electrical energy from sunlight. The use of solar cells is optimized by calculating the required energy capacity to operate the sensors, Arduino board, and early warning system.
Integrating AI in Military Decision-Making: A Review of Opportunities, Risks, and Governance M. Thoriq Fadlullah; Agung Risdhianto; Heru Dewanto
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6925

Abstract

Integrating Artificial Intelligence (AI) into military operations creates a paradigm shift, introducing a profound tension between operational opportunities and severe risks to strategic stability. This paper conducts a systematic literature review to investigate this challenge, focusing on the transformation of Military Decision-Making. The analysis confirms that while AI offers significant capabilities in intelligence and logistics, it also introduces a triad of technical, strategic, and human-centric risks. These risks fuel a global arms race and create a crisis of accountability, particularly with the development of Autonomous Weapons. The central problem identified is a critical "governance gap," where the rapid, geopolitically-driven adoption of military AI has dangerously outpaced the development of effective oversight. This study addresses this gap by synthesising fragmented literature into an integrated, problem-solving framework. It argues that robust Ethical Governance is necessary to respond to these complex challenges. The operationalisation of Meaningful Human Control (MHC) is the cornerstone for closing the "responsibility gap" and ensuring that human agents remain accountable for using force. The paper concludes that a prioritised, multi-layered governance strategy—from short-term national testing standards to a long-term international autonomy treaty is essential. Pursuing AI-driven military advantage without such reforms will lead to unacceptable strategic instability and ethical compromise, undermining the security it intends to enhance.
CNN-Based Identification of Longan Varieties Using Leaf Vein Patterns Pratama, M. Aditya Yoga; Setiawan, Herri; Purnamasari, Evi
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6926

Abstract

Visual classification of longan seedlings remains challenging due to the similarity of characteristics among varieties, particularly in young leaves. This study applies the Convolutional Neural Network (CNN) method to classify five types of longan seedlings—Diamond River, Matalada, Merah, Itoh, and Pingpong—based on leaf vein patterns, which serve as distinctive features. The dataset consists of 1,000 high-resolution images, divided into 900 for training and 100 for testing. The training process includes preprocessing steps such as cropping to focus on vein patterns, resizing to standardize input dimensions, augmentation to enhance data variety, normalization to scale pixel values, and splitting into training and validation sets. Hyperparameter tuning was performed using a grid search, evaluating combinations of learning rate, batch size, and epochs. The best configuration was achieved at the 80th epoch, with a learning rate of 0,0001 and a batch size of 8. The model achieved a validation accuracy of 0,8444 and a loss of 0,3865. During testing, it reached an accuracy of 0,8000, with an average precision of 0,8266, recall of 0,8000, and f1-score of 0,7843. The best performance was observed in the Merah and Matalada classes, while the Diamond class remained challenging due to visual similarities. CNN proved effective for this task, though further improvement is needed for visually similar classes to enhance classification accuracy.
Internet of Things (IoT)-Based Water Quality Monitoring System Design for Tilapia Fish Farming Ponds Erawati, Putu; Prasti, Dianradika; Kriswinarso, Tri Bondan
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6927

Abstract

This study aimed to develop an Internet of Things (IoT)-based water quality monitoring system for tilapia cultivation ponds at Varel Collection. Farmers previously monitored pond water manually, which was often neglected due to their agricultural activities, leading to poor water conditions and negative impacts on fish growth. To overcome this issue, a prototype system was designed to enable real-time monitoring of water parameters using IoT technology integrated with the Blynk platform. The research applied a Research and Development (R&D) approach with a Prototype method. The system was built using NodeMCU ESP32 as the main microcontroller, integrated with several sensors including a pH sensor, DS18B20 temperature sensor, and a turbidity sensor to measure water quality parameters. Data from these sensors are processed and transmitted to the Blynk application, allowing farmers to remotely monitor pond conditions via the internet. Supporting tools included Fritzing for design, Arduino IDE for programming, and hardware components such as adapters, jumper cables, and enclosures. Evaluation by three experts indicated that the system achieved an average feasibility index of 95.93%, categorized as “very good.” These findings show that the IoT-based monitoring system is functional, feasible for aquaculture use, and has strong potential for further development in real-time water quality management.
Maximum Power Point Tracking Achievements and Challenges in Photovoltaic Systems Mousa, Ahmed
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.v3i2.2385

Abstract

The ever-increasing demand for electrical energy in recent decades has necessitated the exploration of alternative energy sources, one of which is solar energy. The most practical means of utilizing solar energy is through the use of a Photovoltaic (PV) system. Nevertheless, the energy harvested by PV modules is constrained by low conversion efficiency, nonlinearity, and susceptibility to weather conditions, such as temperature and irradiance levels. To address these limitations, Maximum Power Point Tracking (MPPT) techniques have been developed to optimize the output of PV systems under specific circumstances. This academic article provides an in-depth analysis of the most widely used MPPT techniques, utilizing both traditional and soft computing methods. The article discusses the fundamental principles and practical applications of these techniques, as well as the challenges associated with MPPT, such as coping with rapidly changing irradiance and partial shading scenarios.
Prototype and Application Implementation of Domestic and Foreign Labor Distribution Amin, Muhammad; Muin , Agus Alim; Rasyidan , Muhammad; Wagino
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.v3i2.2748

Abstract

In today's globalized world, many people are looking for domestic and international work for various reasons, such as higher salaries and better work experience. This application will assist job seekers in finding suitable job vacancies. With their skills and interests, as well as helping companies find candidates that suit their needs, information on job vacancies in the country and abroad. In the initial phase of developing this application, an application prototype will be created that includes the main features of this application. These features include information on job vacancies in the country and abroad, a job search system based on skills and interests, and a system design method using UML (Unified Modeling Language) design. This modeling is a system implementation of how to put a strategy into an image (visual) in the form of a diagram. This model facilitates the making of an application program or implementation and can be used in the long term, not only at this time but continuously and continuously. Because application programs are used for a long time, it is necessary to have a good and precise analysis of planning, design, and modeling, such as flow for application programs. In this study, using the UML method, submitting online job applications, as well as a notification.
The Comparison of Decision Tree and K-Nearest Neighbor Performance for Determining Mustahik Amelia, Noor; Aprianti, Winda
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.v3i2.2953

Abstract

The problem of poverty is one of the fundamental issues of concern to the Indonesian government. One of the methods used by Islam to alleviate poverty is through zakat from Badan Amil Zakat Nasional (BAZNAS). Currently, the distribution of zakat is divided into two, namely in the form of consumptive zakat and productive zakat. Productive zakat is aimed at people who need business capital. To assist zakat managers in managing their funds, a mechanism is needed that can process mustahik data so that it can be selected more quickly and precisely using data mining. In this research, the data mining methods that will be used are K-nearest neighbor (KNN) and Decision Tree. The dataset used in this research is data obtained from BAZNAS and has been preprocessed to obtain a dataset with 7 attributes and 144 records. Decision trees, KNN Manhattan, and KNN Euclidean are used to predict mustahik candidates who are worthy of receiving zakat. The performance of the third method was tested using AUC and confusion matrix namely Accuracy, Precision, Recall, and F1 in each dataset split scenario of 70%:30%, 75%:25%, and 80%:20%. Based on the number of false positive and false negative results, the best performance obtained is KNN Euclidean with a dataset division scenario of 80%:20%.
Fiber Optical Network Damage Detection Passive Splitter 1:8 in ODC uses IOT Technology as a means of Real Time Reporting Asril, Aprinal Adila; Septima, Uzma; Dewi, Ratna; Maria, Popy; Herda, Deri Latika
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.v3i2.2966

Abstract

Fiber optic networks currently have a lot of interest, so a network monitoring system is needed that guarantees quality and speed of repair if mass disruption occurs. in research [1] regarding fiber network damage detection using IoT with the use of a 1:4 splitter and the use of a detector that can work at a wavelength of 650nm so that it can detect damaged cables with output in the software. So in connection with this, the author wants to develop the results of this research by using a 1:8 splitter and carrying out detection using the LDR sensor and NodeMCU ESP32 using IoT (Internet of Things) technology. The ESP32 NodeMCU will receive data in the form of light intensity values ??at each ODC from the LDR sensor. And then sent to a database that is connected directly to the Android application. The cable identification process occurs in three states: normal, warning, and error. The test and analysis results show that the hardware device can work well, with attenuation in the passive splitter cable of 10.28 dB and a light source with a wavelength of 650 nm. Cable detected as damaged is indicated by an output in the software with a delay of 4.56 s.
Installation and Activation of Fiber To The Home (FTTH) Networks and Macrobending Problems in the Feeder Cable Segment Yustini; Asril, Aprinal Adila; Setiawan, Herry; Maria, Popy; Rifka, Silfia
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.v3i2.2967

Abstract

Macrobending is a form of disturbance in Fiber To The Home (FTTH) networks that occurs due to macro-level bending of the cable caused by damage to the fiber optic. Macrobending frequently occurs in FTTH networks within the feeder cables. Feeder cables serve as connectors between the Optical Line Terminal (OLT) and Optical Distribution Cabinet (ODC) in the FTTH system. The occurrence of macrobending in feeder cables affects the quality of the FTTH network. In this study, the impact of macrobending is analyzed based on curvature diameters of 50 cm, 25 cm, and 5 cm on feeder cables before and after FTTH network activation. Before FTTH network activation, the High Super Luminescent Diode (HSL) is used as the input power source, whereas after activation, the input power source comes from the OLT using Small Form-factor Pluggable (SFP) modules. The attenuation (loss) before activation due to macrobending, with curvature diameters of 50 cm, 25 cm, and 5 cm, is found to be 0.02 dB, 0.05 dB, and 0.26 dB, respectively. After activation, the attenuation with the same curvature diameters is measured as 0.01 dB, 0.02 dB, and 0.20 dB, respectively. It is observed that as the curvature diameter decreases, the attenuation increases. The comparison of attenuation before and after network activation doesn't show a significant difference because the input power doesn't affect macrobending, rather it is influenced by the curvature diameter.

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