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All Journal Brilliance: Research of Artificial Intelligence Jurnal Pengabdian Masyarakat Bangsa
Annisa Karima
Universitas Malikussaleh, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Website-Based Text Encryption Simulation with Hill Chiper T. Sukma Achriadi Sukiman; Anni Zulfia; Annisa Karima; Athiyatul Ulya; Muharratul Mina Rizky
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 Anni Zulfia; Tasya Nadhira Ilfa; Zayyani Damia; T. Sukma Achriadi Sukiman; Annisa Karima
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 Athiyatul Ulya; Annisa Karima; T. Sukma Achriadi Sukiman; Anni Zulfia; Rafika Rahmawati
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.
Prediction Of Unemployment Rate Using The Fuzzy Time Series Chen Model Method Annisa Karima; Dahlan Abdullah; Muchlis ABD Muthalib; Nurdin Nurdin; Muhammad Daud
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.
Strategi Pemberdayaan Pemuda dalam Edukasi Literasi Digital untuk Meningkatkan Ketahanan Masyarakat terhadap Penyebaran Berita Hoax Cut Ita Erliana; Muthmainnah Muthmainnah; Athiyatul Ulya; Anni Zulfia; Annisa Karima; Mutammimul Ula
Jurnal Pengabdian Masyarakat Bangsa Vol. 4 No. 4 (2026): Juni
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v4i4.4560

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengimplementasikan strategi pemberdayaan pemuda melalui edukasi literasi digital dalam rangka meningkatkan ketahanan masyarakat terhadap penyebaran berita hoaks. Permasalahan utama mitra adalah rendahnya kemampuan literasi digital, khususnya dalam melakukan verifikasi informasi serta kecenderungan menyebarkan informasi tanpa pengecekan sumber yang memadai. Kegiatan ini dilaksanakan dengan pendekatan pemberdayaan partisipatif yang mengintegrasikan metode case-based learning, peer-to-peer education, pelatihan literasi digital, pendampingan, serta pembentukan komunitas “Duta Anti-Hoaks”. Pelaksanaan kegiatan dilakukan melalui tahapan persiapan, pelatihan, pendampingan, dan evaluasi menggunakan pretest dan posttest untuk mengukur peningkatan kompetensi peserta. Hasil kegiatan menunjukkan adanya peningkatan kemampuan literasi digital pemuda dalam mengidentifikasi hoaks, melakukan verifikasi informasi, serta penggunaan alat pemeriksa fakta (fact-checking tools). Selain itu, terbentuk komunitas “Duta Anti-Hoaks” dan tersusun modul literasi digital berbasis kasus lokal sebagai luaran kegiatan yang mendukung keberlanjutan program. Kegiatan ini menunjukkan bahwa strategi pemberdayaan pemuda melalui integrasi case-based learning dan peer-to-peer education efektif dalam meningkatkan literasi digital serta memperkuat ketahanan masyarakat terhadap penyebaran berita hoaks di tingkat komunitas.
Prediction of Crime Cases in 2025 in India Using the Fuzzy Time Series Chen Model Method Annisa karima; Anni Zulfia; T. Sukma Achriadi Sukiman; Athiyatul Ulya
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.
Sentiment Analysis of TikTok Netizens on Oil Palm Issues in Papua Using KNN Annisa Karima; Naufal Abdulillah; Aril Maulana; Satria Harry Menov; T. Sukma Achriadi Sukiman
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.8073

Abstract

Oil palm plantation in Papua has become a controversial issue that has generated diverse responses from the public, particularly regarding concerns over environmental impacts and ecosystem sustainability. These differing perspectives create the need to comprehensively and objectively understand public perceptions. This study aims to analyze the sentiment of Indonesian netizens toward the policy of oil palm plantation in Papua using a machine learning–based sentiment analysis approach. The research data were collected from user comments on the TikTok platform, which were subsequently processed through preprocessing stages, translation into English, and automatic labeling using TextBlob. The labeled data were then represented using Term Frequency–Inverse Document Frequency (TF-IDF) weighting and classified using the K-Nearest Neighbors (KNN) algorithm. The classification results using the K-Nearest Neighbors (KNN) algorithm indicate that out of a total of 220 data samples, 31 data (14%) were classified as positive sentiment and 189 data (86%) as negative sentiment. The classification process using the K-Nearest Neighbors (KNN) algorithm with the optimal K value of 5 achieved an accuracy of 77.27%, with a precision of 74.90%, recall of 66.00%, and an F1-score of 67.65%. The recall value for the positive class is relatively low, at 38.46%, indicating that the model still faces challenges in correctly identifying all positive data. This limitation is attributed to the imbalance in data distribution and the complexity of language used in social media comments. Nevertheless, the overall classification results suggest that the majority of netizens tend to oppose oil palm plantation in Papua, mainly due to concerns about environmental impacts and ecosystem degradation.
Co-Authors Anni Zulfia Aril Maulana Cut Ita Erliana Dahlan Abdullah Muchlis Abdul Muthalib Muhammad Daud Muharratul Mina Rizky Mutammimul Ula Muthmainnah Muthmainnah Naufal Abdulillah Nurdin Nurdin Rafika Rahmawati Satria Harry Menov T. Sukma Achriadi Sukiman Tasya Nadhira Ilfa Ulya, Athiyatul Zayyani Damia