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This Author published in this journals
All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH International Journal of Health and Pharmaceutical (IJHP) Journal of Technology and Computer (JOTECHCOM)
Alfarizi, Nauval
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Computer Science & IT Economics, Econometrics & Finance Education Nursing Public Health Social Sciences

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Journal : Journal of Technology and Computer (JOTECHCOM)

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Comparative Machine Learning Analysis for Sentiment Classification of Sumatra Disaster 2025 Alfarizi, Nauval; Lydia, Prima; Novelan, Muhammad Syahputra; Putra, Adi; Sinurat, Satria
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Indonesia is highly vulnerable to natural disasters due to its geological position, resulting in extensive disaster-related news coverage that shapes public sentiment. This study presents a comparative machine learning analysis for sentiment classification of online news related to natural disasters in Sumatra during December 2025. The dataset was collected through web scraping from two major Indonesian news portals, like CNN Indonesia and Detik, and categorized into three sentiment classes: negative, neutral, and positive. Sentiment classification was conducted using Naive Bayes, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) algorithms. The results demonstrate that Naive Bayes achieved accuracy values of 0.57 on the CNN Indonesia dataset and 0.61 on the Detik dataset. However, its performance was highly biased toward the dominant negative class, as indicated by low macro-average F1-scores of (0.24) and (0.39). In contrast, SVM showed the most balanced performance by reducing class bias, achieving accuracies of (0.68) and (0.67) with macro-average F1-scores of (0.51) and (0.59), respectively. KNN demonstrated moderate performance, with accuracy values of 0.60 and 0.59, but remained less effective than SVM in handling minority sentiment classes.
Event-Driven Intrusion Detection and Response Automation Using n8n Workflow Platform Alfarizi, Nauval; Rivaldi, Rivaldi
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

This study introduces a server security monitoring system that uses events to detect SSH brute-force attacks. It uses automatic log analysis and sends real-time alerts. To test how well the system works, an experiment was conducted simulating attacks against an SSH service (port 22) without a firewall. Three different situations were tested: normal access, detecting unusual activity, and high-stress attacks. Under normal conditions, the system saw very little traffic: 233 packets, an average of 19 packets per second, and 38 kbps, indicating little impact and no false alarms. As the attacks grew more intense, network traffic increased significantly, reaching 96,997 packets and 76.5 MB of data during high-stress attacks, with an average speed of 1,132 kbps. All 500 brute-force attempts were found and recorded. By combining automated workflows with real-time Telegram alerts, administrators can get timely warnings. The results show that the system is effective, can handle large amounts of data, and is dependable for real-time SSH attack detection and server security monitoring.
Co-Authors Adi Putra Ahmad Suriyadi Muslim Intan Adevia Rosnarita Lydia, Prima Muhammad Amin Muhammad Syahputra Novelan Rivaldi, Rivaldi Sinurat, Satria