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International Journal Of Computer, Network Security and Information System (IJCONSIST)
Published by Universitas Pembangunan Nasional Veteran Jawa Timur
ISSN : -     EISSN : 26863480     DOI : https://doi.org/10.33005/ijconsist.v3i1
Core Subject : Science,
Computer Science & IT
Focus and Scope The Journal covers the whole spectrum of intelligent informatics, which includes, but is not limited to : • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Autonomous Agents and Multi-Agent Systems • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer Interfacing • Business Intelligence • Chaos theory and intelligent control systems • Clustering and Data Analysis • Complex Systems and Applications • Computational Intelligence and Soft Computing • Cognitive systems • Distributed Intelligent Systems • Database Management and Information Retrieval • Evolutionary computation and DNA/cellular/molecular computing • Expert Systems • Fault detection, fault analysis and diagnostics • Fusion of Neural Networks and Fuzzy Systems • Green and Renewable Energy Systems • Human Interface, Human-Computer Interaction, Human Information Processing • Hybrid and Distributed Algorithms • High Performance Computing • Information storage, security, integrity, privacy and trust • Image and Speech Signal Processing • Knowledge Based Systems, Knowledge Networks • Knowledge discovery and ontology engineering • Machine Learning, Reinforcement Learning • Memetic Computing • Multimedia and Applications • Networked Control Systems • Neural Networks and Applications • Natural Language Processing • Optimization and Decision Making • Pattern Classification, Recognition, speech recognition and synthesis • Robotic Intelligence • Rough sets and granular computing • Robustness Analysis • Self-Organizing Systems • Social Intelligence • Soft computing in P2P, Grid, Cloud and Internet Computing Technologies • Stochastic systems • Support Vector Machines • Ubiquitous, grid and high performance computing • Virtual Reality in Engineering Applications • Web and mobile Intelligence, and Big Data
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 105 Documents
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Implementation Of Face Distance, Usage Duration, And Smart Break Monitoring System Using Python-Based Haar Cascade On Digital Device Usage Khairida Octavia Ramadhani; Syuhada Simbolon; Dwi Nina Putri Anakampun; Hermawan Syahputra
IJCONSIST JOURNALS Vol 8 No 1 (2026): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v8i1.203

Abstract

The prolonged use of digital devices with improper viewing distance can lead to eye strain and visual discomfort. This study aims to develop a real-time monitoring system for digital device usage based on digital image processing. The system is implemented using the Python programming language and the OpenCV library, with the Haar Cascade Classifier method used to detect the user's face through a webcam. Distance estimation is performed using single-camera distance estimation based on facial anthropometric data and the width of the detected face in the image, and is stabilized using smoothing techniques. In addition, the system utilizes user age input to determine the recommended viewing distance range, calculates device usage duration, and evaluates the level of usage risk based on distance, age, and usage time. The system provides warnings in the form of visual alerts, beep sounds, break reminder audio, and a Smart Break feature that requires the user to rest for 20 seconds after a certain usage duration is reached. The implementation results show that the system is capable of detecting faces in real time, estimating distance with stable performance, and providing adaptive and responsive usage notifications. Therefore, the proposed system can be used as a simple and effective eye health monitoring tool without requiring additional hardware.
Implementation of a Telegram Bot for Router Monitoring Using Python and the RouterOS API Alya Namira; Dedy Kiswanto; Adinda Soleha
IJCONSIST JOURNALS Vol 8 No 1 (2026): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v8i1.206

Abstract

Network monitoring plays an important role in maintaining the stability and availability of computer network services. Manual monitoring approaches often cause delays in detecting network faults and responding to disruptions. This study implements a MikroTik router monitoring system that integrates Python, the RouterOS API, a Telegram Bot, and a Streamlit dashboard to support real-time fault detection and administrator notification. The system periodically checks router connectivity status and automatically detects when a router transitions between online and offline states. Upon detecting a status change, the system delivers immediate notifications to administrators through Telegram Bot and records the event in a log file for diagnostic purposes. Router metrics including CPU usage, memory availability, uptime, and interface conditions are displayed through a web-based interactive dashboard, providing administrators with a visual interface for monitoring network health. System testing was conducted by simulating router online and offline conditions. Results show that the system successfully detects router status changes, maintains diagnostic logs, and delivers real-time notifications via Telegram and a web-based monitoring interface. This integration of automated fault detection, mobile notification, and web-based visualization offers an effective approach to improving network monitoring responsiveness.
AI-Based Wi-Fi Traffic Profiling and Anomaly Detection System Micael Zecsen Saragih; Dedy Kiswanto; Jhon Gabriel Simarmata; Bryant Tinambunan
IJCONSIST JOURNALS Vol 8 No 1 (2026): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v8i1.207

Abstract

Abstract— Network traffic monitoring plays an important role in maintaining the availability, reliability, and performance of modern network infrastructures. The increasing volume and complexity of network traffic require monitoring solutions that provide real-time visibility, traffic profiling, anomaly detection, and comprehensive usage analysis. This study proposes an AI-based Wi-Fi network traffic monitoring and analysis system that integrates ntopng for traffic collection, Python-based analytical modules, Prometheus for metrics aggregation, Grafana for visualization, K-Means Clustering for traffic profiling, and Isolation Forest for anomaly detection. The system was implemented in a wireless network environment using real network traffic data collected through the ntopng REST API. The collected traffic data were analyzed to identify traffic patterns, assess traffic severity, categorize network usage activities, and generate monitoring metrics for real-time visualization. The results demonstrate that the proposed system successfully performs traffic clustering, anomaly detection, usage categorization, dashboard-based monitoring, and automated alert integration. Furthermore, the generated analytical information provides insights into network utilization patterns that may support future Quality of Service (QoS) implementation, bandwidth allocation strategies, and intelligent network management. The integration of machine learning techniques with modern monitoring platforms contributes to more effective and centralized network monitoring.
Development of a Sensitive-Data Monitoring System for Witness and Victim Protection: Role-Based Security Integration and Usability Evaluation Erikson Putra Perdana Lumban Tobing; Lamhot Sitorus; Zakarias Situmorang
IJCONSIST JOURNALS Vol 8 No 1 (2026): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v8i1.218

Abstract

Data on applications, case reviews, and protection services for witnesses and victims at the Medan Representative Office of the Witness and Victim Protection Agency (LPSK) had previously been managed through a legacy application built without a modern framework, which constrained data-handling efficiency, feature coverage, and the security controls required for information tied directly to personal safety. Existing literature widely discusses public-service monitoring in general terms but rarely demonstrates concrete, tested technical safeguards for highly sensitive data such as the identities of protected individuals. This study develops a web-based monitoring platform on the Laravel framework that consolidates the application, review, service, and financial workflows into a single system, protected by layered security consisting of Role-Based Access Control (RBAC), role-based data masking, Bcrypt password hashing, CSRF protection, rate limiting, UUID-based routing, and audit trails. A Research and Development (R&D) approach following the 4D model (Define, Design, Develop, Disseminate) was used. Black-box functional testing across 35 scenarios and security testing across 10 scenarios each achieved a 100% success rate, while a User Acceptance Test with 5 respondents at LPSK Medan produced a mean System Usability Scale (SUS) score of 93.6 (Grade A, Excellent). The principal contribution of this work is the integrated combination of RBAC, role-based data masking, and audit trails, implemented and empirically validated within a highly sensitive government information system. System scalability was not verified through load testing, and the security evaluation did not include independent penetration testing, both of which remain limitations and directions for future work.
Sentiment Analysis of Google Gemini Reviews Using K-Nearest Neighbor and Particle Swarm Optimization Siraj Fatwa Sidqy; Yunus Widjaya
IJCONSIST JOURNALS Vol 8 No 1 (2026): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v8i1.221

Abstract

Google Gemini, Google's generative AI assistant, receives a large volume of user reviews on the Google Play Store daily, making manual reading of user opinions impractical. This study analyzes the sentiment of Indonesian-language Google Gemini reviews using K-Nearest Neighbor (KNN) as the baseline classifier, then optimizes the k parameter through Particle Swarm Optimization (PSO) to examine its effect on classification performance. A total of 5,000 reviews were collected via web scraping using the Google Play Scraper library between 1 June and 1 September 2026, followed by preprocessing (duplicate removal, cleaning, case folding, normalization, tokenization, and stopword removal), leaving 2,858 labeled reviews, of which 2,000 were used for modeling. Negative, Neutral, and Positive labels were assigned automatically from each reviewer's star rating. Text was represented using CountVectorizer, producing 2,282 features, split into 1,600 training and 400 testing samples via stratified sampling. The baseline KNN model with k=5 achieved 79.50% accuracy, while PSO selected k=4 with a best cost of 0.188125, yielding a KNN-PSO accuracy of 80.75%, a 1.25 percentage-point increase. This improvement was accompanied by a rise in macro F1-score from 0.441 to 0.458 and weighted F1-score from 0.765 to 0.780, mainly driven by the Negative and Positive classes, while the Neutral class remained undetected by both models (F1=0). These findings indicate that PSO optimization yields a consistent but limited improvement, and does not resolve the underlying difficulty of identifying

Page 11 of 11 | Total Record : 105

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