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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Brilliance: Research of Artificial Intelligence
Sukiman, T. Sukma Achriadi
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other

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Feature Extraction Method GLCM and LVQ in Digital Image-Based Face Recognition Sukiman, T. Sukma Achriadi; Suwilo, Saib; Zarlis, Muhammad
Sinkron : jurnal dan penelitian teknik informatika Vol. 4 No. 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (399.88 KB) | DOI: 10.33395/sinkron.v4i1.10199

Abstract

The face is one of the media to identify someone, a human face has a very high level of variability. Many methods have been introduced by researchers and scientists in recognizing one's face, one of the methods introduced is the Feature Extraction of Gray Level Co-Occurrence Matrix (GLCM) and Learning Vector Quantization (LVQ). GLCM feature extraction is used for data extraction/learning process whereas a data analysis process (face recognition, cropping and storing data) the LVQ method is used for the data training process where the data that has been processed in GLCM feature extraction which still has large dimensions are processed to be smaller dimensions. So this test uses data of 190 photos and gets a match of 90%, the authors conclude that the GLCM feature extraction and LVQ method can very well recognize faces contained in the database.
Prediction of Crime Cases in 2025 in India Using the Fuzzy Time Series Chen Model Method karima, Annisa; Zulfia, Anni; Sukiman, T. Sukma Achriadi; Ulya, Athiyatul
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.5745

Abstract

India's natural beauty and culture, which attract the attention of international tourists, are less able to increase tourist visits due to high crime cases. Tourists' fear of visiting the country has a direct impact on decreasing economic turnover, so the local economy has become very low. Predictions of criminal cases aim to provide an overview of cases that will occur in the next period, therefore the government can take appropriate policies to reduce crime cases. These predictions enable policymakers to plan strategic and data-based preventive measures. The method used is the Fuzzy Time Series Model Chen, because this method can overcome data uncertainty, and offers simplicity and ease in application. Valid and credible criminal statistics data in India is obtained from the site www.kaggle.com. A trusted platform that provides various quality datasets. This data will be used as a basis for the analysis and prediction of criminal cases in India. The results of this research show that in the range of 60 months from January 2020 to December 2024 using the Fuzzy Time Series Chen Model method to predict the number of criminal cases in India produced predictions in January 2025 with cases of 188.36 cases with a MAPE error ratio of 9.08% which is included in outstanding forecasting category.
Effectiveness of Using ArchiCAD in Interactive 3D Visualization in Building Drawing Engineering Learning Media Syahputra, Dinur; Sandy, Cut Lika Mestika; Sukiman, T. Sukma Achriadi; Manurung, Ericky Benna Perolihin; Rizal, Reyhan Achmad
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6342

Abstract

This study aims to analyze the effectiveness of using ArchiCAD software as a tool for interactive 3D visualization in the context of learning media for building drawing engineering. Traditional methods of teaching technical drawing often rely on two-dimensional representations, which can limit students’ spatial understanding and comprehension of complex architectural forms. By integrating ArchiCAD, a Building Information Modeling (BIM)-based software, students are exposed to a more immersive and realistic learning experience, enabling them to visualize construction elements more clearly. The research employs a quasi-experimental method involving two groups: an experimental group using ArchiCAD-based interactive media and a control group using conventional methods. Data collection was conducted through pre-tests and post-tests, as well as student perception questionnaires. The results indicate a significant improvement in the learning outcomes of the experimental group, both in terms of cognitive understanding and design skills. Furthermore, student responses show a high level of satisfaction and engagement when using 3D interactive media. These findings suggest that ArchiCAD can be effectively implemented as a digital learning medium in vocational and technical education settings, especially in the field of architectural drawing. The study recommends broader integration of BIM-based tools to support competency-based learning and enhance the quality of engineering education.
Website-Based Text Encryption Simulation with Hill Chiper Sukiman, T. Sukma Achriadi; Zulfia, Anni; Karima, Annisa; Ulya, Athiyatul; Rizky, Muharratul Mina
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.v5i2.5757

Abstract

Data security has become increasingly crucial in the modern digital era, where almost all types of information ranging from text, images, to audio are stored and exchanged in digital form through open networks. The rapid growth of internet-based communication makes data highly vulnerable to interception, modification, or misuse by unauthorized parties. Cryptography is one of the most effective solutions to address these challenges. Among the classical cryptographic techniques, the Hill Cipher remains relevant today because it is based on linear algebra and matrix transformations, which provide a strong mathematical foundation and can be adapted for modern computational implementation. In this study, a web-based application was developed using the Python Flask framework to implement the Hill Cipher algorithm. The application enables users to perform both encryption and decryption of text and images through an interactive interface. Users can input plaintext and key matrices, and the system processes the data to produce encrypted or decrypted outputs in real time. This design not only demonstrates the practicality of applying classical cryptographic concepts with contemporary web technologies but also serves as a valuable educational tool. The results show that the application performs effectively, producing accurate outputs, while also supporting user learning in understanding encryption–decryption processes and guiding efforts to secure digital information.
AI Decision Support for Demand Forecasting and Retail Stock Using Random Forest Zulfia, Anni; Ilfa, Tasya Nadhira; Damia, Zayyani; Sukiman, T. Sukma Achriadi; Karima, Annisa
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.v5i2.5901

Abstract

Out-of-stock or excess inventory is a major challenge in retail supply chain management, especially in dynamic urban areas. This stock imbalance not only causes financial losses, but can also reduce customer satisfaction due to products being unavailable when needed. This study developed an artificial intelligence (AI)-based decision support system using the Random Forest algorithm to predict daily demand in retail stores. The model was trained using historical sales data that included various variables such as date, product category, and previous sales trends. After the training process, the model was implemented in the form of an interactive web application using Streamlit, which allows users to easily access the system through a browser without the need for special installation. Testing results show that the model is capable of predicting demand for the next 7 days with a fairly good level of accuracy, as indicated by a Mean Absolute Error (MAE) value of ±4.613 units per day. This application not only provides demand predictions but also presents data visualizations and automatic restocking recommendations based on the prediction results. Thus, this system is expected to help store managers make more accurate, efficient, and data-driven restocking decisions. Additionally, the use of Streamlit simplifies the process of distributing the system widely and enhances accessibility for end-users, including those without a technical background. This research opens opportunities for further development through the integration of real-time data and other AI methods to improve prediction accuracy in the future.
Information Security Risk Analysis Using ISO 31000:2018 and ISO 27001:2022 Ulya, Athiyatul; Karima, Annisa; Sukiman, T. Sukma Achriadi; Zulfia, Anni; Rahmawati, Rafika
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.v5i2.6564

Abstract

Information system risk audits are an important step in ensuring the security, effectiveness, and efficiency of the systems used by organizations. However, the fast advancement of information and communication technologies has made information?security threats more intricate, arising not only from internal sources like employee carelessness but also from external sources such as cyber?attacks, malware, and data?theft. This study aims to analyze information security risks at the Central Statistics Agency (BPS) of Lhokseumawe by referring to two international standards, namely ISO/IEC 27001:2022 and ISO 31000:2018. The research approach used is descriptive qualitative with a case study method. Data collection techniques were conducted through interviews, observations, and document studies. The results of the study indicate that there are still various security gaps, both technical and non-technical, such as weak system authentication, the absence of adequate security policies, and the lack of incident handling procedures. This study successfully compiled a risk register containing 30 types of risks along with their causes, impacts, likelihood levels, and relevant mitigation recommendations. Improvement recommendations include strengthening technical controls, updating information security policies, enhancing human resource capacity, and conducting regular internal audits. The results of this study are expected to serve as a reference for strengthening information security systems in a systematic and standardized manner within the BPS environment.
Co-Authors Damia, Zayyani Ilfa, Tasya Nadhira Karima, Annisa Manurung, Ericky Benna Perolihin Muhammad Zarlis, Muhammad Rafika Rahmawati Rizal, Reyhan Achmad Rizky, Muharratul Mina Saib Suwilo Sandy, Cut Lika Mestika Syahputra, Dinur Ulya, Athiyatul Zulfia, Anni