Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal Eduvest - Journal of Universal Studies Journal of Innovation and Operational System
Komarudin Komarudin
Universitas Catur Insan Cendikia
Aerospace Engineering Computer Science & IT Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Neuroscience Social Sciences Transportation

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Exploring The Effectiveness of Artificial Intelligence in Detecting Malware and Improving Cybersecurity in Computer Networks Komarudin Komarudin; Isma Elan Maulani; Tedi Herdianto; Medika Oga Laksana; Dwi Febri Syawaludin
Eduvest - Journal of Universal Studies Vol. 3 No. 4 (2023): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v3i4.793

Abstract

Malware, in particular, has been identified as a major cy-bersecurity challenge due to its ability to infiltrate computer networks, steal sensi-tive data, and cause major damage to computer systems. The purpose of this study was to explore the effectiveness of artificial in-telligence in detecting malware and improving cybersecurity in computer net-works. Success rate in detecting and preventing malware attacks on computer networks using AI-based methods. The time it takes to detect and prevent malware attacks on computer net-works using AI-based cyber protection methods. Furthermore, the selection of two types of malware that are often found on computer networks, namely Trojans and Worms, and data sampling was then test-ed on a simulation system. In this study, three different AI techniques were applied, namely Support Vector Machine, Neural Network , and Decision Tree to detect malware on computer networks.
Optimizing Operational Efficiency Through Technological Innovation: A Comparative Analysis Across Industries Komarudin Komarudin
Journal of Innovation and Operational System Vol. 1 No. 1 (2024): Journal of Innovation and Operational System
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jiosjournal.v1i1.1

Abstract

This study examines the role of technological innovation in optimizing operational efficiency across manufacturing, healthcare, logistics, and finance sectors. Using a quantitative approach, data were collected from 200 companies, assessing key operational metrics such as production time, error rates, and cost savings. Findings indicate that manufacturing and logistics sectors experienced the greatest efficiency gains from technologies like automation and the Internet of Things (IoT), with reductions in production time averaging 18% and 15%, respectively. In contrast, the healthcare and finance sectors faced greater regulatory challenges, moderating the immediate impact of technologies like artificial intelligence (AI) and automation. These results align with Rogers’ Diffusion of Innovation Theory, highlighting sectoral variability in technology adoption and efficiency outcomes. The study suggests that phased technology implementation and industry-specific compliance strategies can enhance efficiency gains in regulated sectors. This research provides valuable insights for corporate strategists and policymakers, advocating for tailored approaches to technology adoption to maximize operational benefits. Future studies could explore the impact of innovation on sub-industries within these broader sectors.
Co-Authors Dwi Febri Syawaludin Isma Elan Maulani Medika Oga Laksana Tedi Herdianto