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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 Articles sent to the Brilliance may not be published elsewhere.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 548 Documents
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Comparison of SVM and KNN Methods for the Integratin of MyIndiHome into MyTelkomsel Application Siagian, Harul Risina; Setiawan, Dedy; Abidin, Zainil
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.v5i2.7234

Abstract

This study aims to analyze user sentiment toward the merger of the MyIndiHome application into the MyTelkomsel platform conducted by PT Telkom Indonesia. In the digital era, the integration of these two customer service applications represents a strategic step to create a unified digital ecosystem. However, this merger has also generated diverse user responses, reflected in various reviews on the Google Play Store. To analyze these opinions, 1,556 user reviews were collected using the web scraping technique. The preprocessing stage included cleaning, tokenizing, filtering, normalization, stemming, and the application of the Synthetic Minority Over-Sampling Technique (SMOTE) to address class imbalance. Two machine learning algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), were applied to classify sentiments into positive, negative, and neutral categories. The experimental results showed that SVM achieved higher accuracy (86.2% before SMOTE and 84.9% after SMOTE) compared to KNN (83.7% before SMOTE and 67.6% after SMOTE). These results indicate that SVM performs more effectively and consistently in handling high-dimensional text data than KNN. Therefore, SVM is considered a more reliable algorithm for sentiment classification in this context. The findings provide valuable insights for PT Telkom Indonesia in understanding user perceptions, improving service quality, and enhancing user experience following the digital integration of MyIndiHome into MyTelkomsel.
IoT-Based Electrical Power Consumption Monitoring System in Households Using ESP32 and PZEM-004T Hidayat, Kemas Muhammad Wahyu; Al-Faris, Muhammad Ghozi
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.v5i2.6368

Abstract

Electricity consumption in the household sector is often poorly controlled, leading to energy waste and increased electricity bills. To address this issue, this study presents the design and implementation of an electricity consumption monitoring system based on the Internet of Things (IoT) using the ESP32 microcontroller and PZEM-004T sensor. The system allows real-time monitoring of electrical parameters such as voltage, current, power, and energy, with data displayed on digital devices like smartphones or computers.  Measurement data from the sensor is transmitted wirelessly to an IoT platform via Wi-Fi, enabling users to monitor electricity usage anytime and anywhere. A prototype method was used in this research, covering hardware and software design, sensor integration, and system testing.The testing results show that the system effectively provides accurate and responsive electricity usage data. With this system, users can identify usage patterns, detect high-power appliances, and make informed decisions to improve energy efficiency. The ability to access real-time data helps prevent energy waste and control monthly costs more effectively.Beyond individual benefits, this system also supports wider energy conservation initiatives by promoting conscious energy consumption behavior. The integration of IoT technology in household energy management demonstrates a practical solution for creating smarter and more sustainable living environments. This study confirms the potential of IoT-based systems to enhance energy awareness and support efforts in reducing overall electricity consumption.
Design and Implementation of Electronic Queue System at Walenrang Health Center for Service Optimization Sugiman, Rizky Amalia; Prasti, Dianradika; Suparman, Suparman
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.v5i2.6929

Abstract

This study aims to design, develop, and implement an Android-based electronic queuing system to enhance the effectiveness, efficiency, and overall quality of healthcare services at Walenrang Community Health Center. The manual queuing system previously applied often caused patient congestion, long waiting times, service delays, and discomfort for both patients and staff. To overcome these problems, an electronic queuing system was created, allowing patients to take queue numbers online, monitor the real-time status of the queue, access detailed service information, and view their visit history conveniently. This system was developed using a Research and Development (R&D) approach, following the waterfall model as the software development method. Evaluation of the system involved three expert validators, resulting in a validation score of 95.17%, which classifies the system as highly feasible for implementation. Furthermore, usability testing with 12 user respondents revealed an average satisfaction score of 94 out of 100, indicating a very high level of user satisfaction regarding ease of use, speed of access, and accuracy of queue information. Based on these results, the implemented system has proven effective in improving the efficiency of healthcare service delivery while providing greater convenience, comfort, and accessibility for the community, thereby supporting a more organized and patient-friendly healthcare environment.
Decision Support System for Movie Trend Analysis in 2025 Using the Naïve Bayes Algorithm Herwandi, Rian Risnandar; Wibowo, Ari Purno Wahyu
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.v5i2.7092

Abstract

The entertainment industry faces high financial risks in movie production, while large-scale online data such as IMDb provide opportunities for predictive analysis. This study aims to apply machine learning, specifically the Naïve Bayes algorithm, to predict movie success and analyze emerging audience trends through a Decision Support System (DSS). Recent IMDb datasets (2022–2025) were collected and preprocessed. Movies were classified into “Successful” and “Less-successful” categories based on audience ratings and vote thresholds. The Naïve Bayes algorithm was chosen for its efficiency and interpretability in handling textual and categorical data. Model performance was evaluated using accuracy, precision, and recall. The model achieved 95.7% accuracy with balanced precision and recall. Trend analysis showed that Action movies consistently had the highest likelihood of success, while genres such as Crime and Biography demonstrated moderate performance. The DSS framework provided useful insights for producers and distributors in reducing risks. Naïve Bayes proved effective for sentiment-driven prediction when embedded in a DSS, supporting strategic decision-making in the film industry. Despite limitations in feature independence and reliance on IMDb, the study shows the potential of machine learning–based tools to align production with audience preferences. Future research should expand to multi-platform datasets and advanced models to enhance predictive robustness and industry applicability.
Application of a Smart Farming Monitoring System to Optimize Vegetable Production in North Aceh Meiyanti, Rini; Nunsina, Nunsina; Fitria, Rahma; Munauwar, Muhammad Muaz
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.v5i2.7285

Abstract

This research aims to design and implement a smart farming monitoring system tailored to the local conditions of North Aceh, optimizing the production of leading vegetables and facilitating sustainable agricultural transformation. In line with the national agenda toward the digitalization of the agricultural sector, this research is part of a concrete effort to encourage the adoption of smart farming technology at the local level. North Aceh Regency has great horticultural potential, but it is not yet optimal due to the minimal application of technology. This research supports the development of agriculture based on local potential. The study also promotes a participatory and educative approach to increase farmers' digital literacy and reduce the technology gap between conventional and modern technology-adopting farmers. The Smart Farming monitoring system was successfully implemented using soil moisture, air temperature, soil pH, and light intensity sensors integrated into a web-based dashboard and mobile application. The implementation of this system was able to increase vegetable productivity by 18–22%, especially for mustard greens, chili, and tomatoes, compared to conventional methods. The system also contributed to the efficient use of resources, shown by a 25% savings in irrigation water and a 15% reduction in the use of chemical fertilizers. The farmer response was quite positive, although there are still challenges related to digital literacy among some older farmers. Overall, the implementation of Smart Farming in North Aceh Regency had a real impact on increasing productivity and cost efficiency while supporting sustainable agriculture in line with the SDGs.
Detection Of Left-Behind Bags Based On YOLOv11 And DeepSORT Budi, Raden George Samuel; Wijaya, Novan
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.v5i2.7349

Abstract

Incidents of bags being left behind in public facilities such as transportation hubs, offices, and educational environments continue to pose security challenges, especially when monitoring relies solely on human operators. To address the limitations of manual CCTV observation, this study presents an automated system capable of identifying abandoned bags by integrating the YOLOv11n detection model with the DeepSORT tracking algorithm. The dataset used consists of 1000 annotated bag images, combined with a pre-trained YOLOv11 human detector. Prior to training, image preprocessing and augmentation were applied to ensure that the model remained robust under varying illumination, distance, and viewpoint conditions. Model training was carried out in Google Colab using PyTorch with 20 epochs, a learning rate of 0.002, and a batch size of 8. Experimental results indicate that YOLOv11n delivers strong detection performance, achieving a mAP@0.5 of 0.787, a precision score of 0.837, a recall of 0.690, and an F1-Score of 0.755. When combined with DeepSORT, the system operates efficiently in real time, reaching an average of 28.30 FPS with a latency of 35.34 ms per frame. The system effectively distinguishes bags that are separated from their owners through correlation analysis between human and bag movements. Overall, the proposed approach is capable of supporting real-time surveillance needs, although future enhancement of dataset diversity and adaptive thresholding is recommended to improve detection in more complex environments.
Prototype of Automatic Tomato Plant Watering Monitoring Tool Based on Telegram Using NodeMCU ESP8266 Juliansyah, Achmat; Fahmi, Muhammad; Rangan, Andi Yusika
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.v5i2.7293

Abstract

The goal of this research is to design and develop a Telegram-based watering pump. This research aims to control and monitor soil moisture in tomato plants. To address this issue, an approach utilizing Arduino technology was used to create an attractive and efficient solution. This study analyzed the various components used in a Telegram-based tomato plant watering system. These components include the MCU8266 node, which connects or translates to a soil moisture sensor system used to measure soil moisture in tomato plants. The results of this research resulted in the successful design and implementation of a telegram-based tomato plant watering system. This system makes it easier for users to ensure the aerator remains operational. Black-box and white-box testing methods were used in this study to verify the system's functionality and security. Thus, this research provides a positive contribution to plantations in the Tanjung Batu PLTGU area through the application of innovative and efficient Arduino technology.
Prediction Of Unemployment Rate Using The Fuzzy Time Series Chen Model Method Karima, Annisa; Abdullah, Dahlan; Muthalib, Muchlis ABD; Nurdin, Nurdin; Daud, Muhammad
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.v5i2.7310

Abstract

Unemployment is a significant socio-economic problem in Lhokseumawe City that requires serious attention from policymakers. The unemployment rate fluctuates from year to year, making accurate forecasting an important aspect in formulating effective strategies and policies to reduce unemployment. One method that can be used to analyze and forecast time series data with uncertainty is the Fuzzy Time Series (FTS) method, which applies fuzzy logic concepts to handle vague and imprecise data patterns. In this study, the Fuzzy Time Series method is applied to predict the number of unemployed people in Lhokseumawe City. The data used are historical unemployment data over a period of 10 years, from 2013 to 2022. The research process begins with defining the universe of discourse (U), determining the number and length of interval classes, defining fuzzy sets on U, and fuzzifying the unemployment data. Furthermore, Fuzzy Logical Relationships (FLR) are identified and grouped into Fuzzy Logical Relationship Groups (FLRG). The defuzzification process is then carried out to obtain crisp values, followed by forecasting calculations.The analysis was conducted using the RStudio application. The forecasting results show that the predicted number of unemployed people in 2023 is 10,514.125, which is rounded to 10,514 people. The accuracy of the forecasting model is evaluated using Mean Absolute Percentage Error (MAPE) and Average Forecasting Error Rate (AFER), both of which yield values of 6.70%. Since the MAPE and AFER values are less than 10%, the forecasting results can be categorized as very good and reliable for decision-making purposes.
TreeRTTSys: A Low Cost Sensor To Measure Tree Trunk Quality Using Strain Gauge Sensors Candra, Rudi Arif; Ilham, Dirja Nur; Budiansyah, Arie
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.v5i2.7324

Abstract

Tree health monitoring is essential to ensure environmental safety, sustainability, and the prevention of hazards caused by structurally weakened trees. Visual inspection alone is often insufficient to detect internal defects such as decay or reduced mechanical strength within tree trunks. This study presents the design and implementation of TreeRTTSys, a low-cost sensor-based system for evaluating tree trunk quality using strain gauge and load cell sensors integrated with an Arduino microcontroller. The proposed system aims to measure tensile force characteristics of tree trunks as an indicator of structural integrity and mechanical performance. The experimental method was employed by conducting tensile tests on five different types of tree trunks, namely Meranti, Beringin, Rambutan, Durian, and Kapok. A load cell sensor combined with an HX711 signal conditioning module was used to acquire force data, which were processed and recorded in real time by an Arduino-based data acquisition system. The applied tensile load and resistance duration were analyzed to evaluate the strength and deformation behavior of each wood type. The results show significant variation in tensile strength and load resistance among the tested tree species. Meranti wood exhibited the highest tensile strength of 11.13 kN and the longest resistance time of 151 seconds, indicating superior load-bearing capacity and stability. Rambutan wood demonstrated high ductility, sustaining tensile loading for 149 seconds despite a lower maximum force. In contrast, Kapok and Durian woods showed relatively low tensile resistance and shorter failure durations.These findings confirm that the proposed TreeRTTSys is capable of accurately capturing the mechanical behavior of tree trunks in real time. The system offers a reliable, cost-effective solution for tree health assessment, with potential applications in urban forestry management, environmental monitoring, and preventive safety inspections.
Back-End Geographic Information System Development for Linguistic and Literary Mapping in Jambi Province Hutabarat, Cagivamito Tadashi; Utomo, Pradita Eko Prasetyo; Khaira, Ulfa
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.v5i2.7384

Abstract

Indonesia possesses a rich diversity of regional languages and literature, including 718 recorded languages and 965 literary works nationally. Jambi Province, with its seven local languages and abundant oral traditions, requires more effective preservation efforts. Currently, available information is limited to static physical maps that are difficult to access. This study aims to develop a web-based Geographic Information System to digitally, interactively, and flexibly map the distribution of languages, literature, and scripts in Jambi. The system was developed using the System Development Life Cycle (SDLC) approach with an Incremental model, allowing for gradual development and continuous adjustments. The primary focus was on back-end development, including the database, business logic, and server. Functional testing was conducted using black-box testing, while non-functional performance evaluation was carried out through load testing using k6 on the main features, simulating 50 Virtual Users (VUs). Test results indicated that the system was stable and responsive, with a 0.00% failure rate, average response times of 59.08–68.74 ms, and a P95 not exceeding 106 ms. The system was developed in two increments: a general user interface and an administrator dashboard, enabling efficient management of language, literature, script, announcement, and feedback data. The implementation of this digital platform enhances information accessibility, supports the Language Office of Jambi Province in data dissemination, and contributes to the preservation of regional cultural heritage for the public and researchers.

Page 52 of 55 | Total Record : 548

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