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All Journal Jurnal Sains Informatika Terapan (JSIT) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika
Halizzah, Nur
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Improvement of IoT Security with a Machine Learning-Based Intrusion Detection System Approach Halizzah, Nur; Dewi, Indah Kusuma; Fernandes, Atman Lucky; Saro, David
Jurnal Responsive Teknik Informatika Vol. 8 No. 02 (2024): Jurnal Responsive Teknik Informatika
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v8i02.1001

Abstract

The development of the Internet of Things (IoT) has brought convenience to various aspects of life, but it also presents significant challenges regarding cybersecurity. One solution to address this issue is the development of an Intrusion Detection System (IDS) based on machine learning. This study aims to design an efficient and adaptive IDS for IoT environments using machine learning algorithms such as Random Forest and Support Vector Machine (SVM). The methodology includes system design, data collection, algorithm selection, model training, and system performance evaluation. The results show that Random Forest and SVM algorithms are effective in detecting attacks such as Distributed Denial of Service (DDoS) and malware, with a relatively high accuracy rate. However, the main challenges faced are the need for representative datasets and computational efficiency issues on resource-constrained IoT devices. This study concludes that machine learning-based intrusion detection systems can improve IoT security by accurately detecting cyber-attacks. Further development is expected to address efficiency constraints and enhance the system's reliability in facing increasingly complex threats.
Improvement of IoT Security with a Machine Learning-Based Intrusion Detection System Approach Halizzah, Nur; Dewi, Indah Kusuma; Fernandes, Atman Lucky; Saro, David
Jurnal Responsive Teknik Informatika Vol. 8 No. 02 (2024): Jurnal Responsive Teknik Informatika
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v8i02.1001

Abstract

The development of the Internet of Things (IoT) has brought convenience to various aspects of life, but it also presents significant challenges regarding cybersecurity. One solution to address this issue is the development of an Intrusion Detection System (IDS) based on machine learning. This study aims to design an efficient and adaptive IDS for IoT environments using machine learning algorithms such as Random Forest and Support Vector Machine (SVM). The methodology includes system design, data collection, algorithm selection, model training, and system performance evaluation. The results show that Random Forest and SVM algorithms are effective in detecting attacks such as Distributed Denial of Service (DDoS) and malware, with a relatively high accuracy rate. However, the main challenges faced are the need for representative datasets and computational efficiency issues on resource-constrained IoT devices. This study concludes that machine learning-based intrusion detection systems can improve IoT security by accurately detecting cyber-attacks. Further development is expected to address efficiency constraints and enhance the system's reliability in facing increasingly complex threats.
Model Graph Untuk Penataan Sistem Transpormasi Berbasis Algoritma Travelsal Dan MST Halizzah, Nur; Yoga, Muhammad; Al - Kahfi, Muhammad Zidane; Rosidhin, Muhammad
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.654

Abstract

In order to connect important places with the least amount of travel time and operating expense, an effective urban transportation system needs to have well-planned routes. With a case study on the Trans Batam bus system in Indonesia, this paper introduces a graph-based model for organizing transportation networks utilizing traversal algorithms and the Minimum Spanning Tree (MST) technique. Each node in this model denotes a bus stop or an important hub for activity, and the edges show potential routes between them, weighted by variables like distance, expected journey time, or road conditions..Depth-First Search (DFS) and Breadth-First Search (BFS) are two traversal algorithms that are used to map and investigate the entire set of possible pathways within the network. The most effective connection paths that connect all stops with the least cumulative weight and no cycles are then found using MST algorithms like Kruskal or Prim. The model's ability to eliminate redundant routes, identify the shortest routes, and provide affordable options for route optimization and corridor expansion is demonstrated by simulation and analysis.The results suggest that this graph-based planning framework offers a practical and adaptive solution for improving public transportation efficiency. It may serve as a valuable reference for transportation authorities and urban planners seeking to optimize mass transit systems in rapidly growing cities like Batam.
Co-Authors Al - Kahfi, Muhammad Zidane Fernandes, Atman Lucky Indah Kusuma Dewi, Indah Kusuma Rosidhin, Muhammad Saro, David Yoga, Muhammad