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Dery Yuswanto Jaya
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KOMNET : Jurnal Komputer, Jaringan dan Internet
Published by Politeknik Negeri Tanah Laut
ISSN : -     EISSN : 30637694     DOI : -
Core Subject : Science, Education,
Computer Science & IT Mathematics Other
KOMNET (Jurnal Komputer, Jaringan dan Internet), E-ISSN: 3063-7694 is a open access journal published by the Research Division, POLITALA Institute, Indonesia, The KOMNET (Jurnal Komputer, Jaringan dan Internet) is a scientific journal which is a forum for scholars and experts from various countries to publish scientific articles about important aspects in the fields of Computers, Networks, Information Technology and the Internet, is a peer-reviewed journal published two times a year (June and December). The KOMNET invites manuscripts in the various topics include, Computer Networking, IoT, Mobile Technology and its applications, Artificial Intelligence, Machine Learning, Deep Learning, Computational Intelligence, Natural Language Processing, Big Data, Data Mining, Data Warehouse and its application, Computer Vision, Digital Image Processing and its applications, Information System and its application, Geographic Information System and its application, Graphics, Multimedia and its applications, Computers and Informatics in Education, Learning Models and ICT Teaching Model, Development of Learning Media Based on Informatics Technology. KOMNET Journal (Jurnal Komputer, Jaringan dan Internet) is focusing on publishing original research, communication, and notes as a result of original and high quality research. The journal also invites well known researchers to write a review paper. KOMNET accepts high quality papers in the field of informatics, computer science, informatics education, and technology. The scope of KOMNET encompasses but not limited to the following: Computer Networking, IoT, Mobile Technology and its applications Artificial Intelligence, Machine Learning, Deep Learning Computational Intelligence, Natural Language Processing Big Data, Data Mining, Data Warehouse and its application Computer Vision, Digital Image Processing and its applications Information System and its application Geographic Information System and its application Graphics, Multimedia and its applications Computers and Informatics in Education Learning Models and ICT Teaching Model Development of Learning Media Based on Informatics Technology
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 1 Documents
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Search results for , issue "Vol. 4 No. 1 (2025)" : 1 Documents clear
Network Intrusion Detection Using Machine Learning in Network Intrusion Detection Systems (NIDS) Jansen, Arnoldus; Yuswanto, Dery; Styawan, Budi; Girinata, I Made Candra
KOMNET : Jurnal Komputer, Jaringan dan Internet Vol. 4 No. 1 (2025)
Publisher : Pusat Penelitian dan Pengabdian Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/yt59ac51

Abstract

Computer network security has become a crucial aspect as dependence on network-based services increases. One important mechanism in maintaining network security is the Network Intrusion Detection System (NIDS), which functions to detect suspicious activity or attacks on network traffic. The traditional signature-based approach has limitations in detecting new attacks (zero-day attacks). Therefore, this study proposes the application of Machine Learning and Deep Learning methods to improve network intrusion detection capabilities. The CIC-IDS2017 dataset was used as the data source because it represents various types of modern network attacks. The research stages included data pre-processing, feature selection, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The models used include Random Forest as a representation of Machine Learning and Long Short-Term Memory (LSTM) as a representation of Deep Learning. The results show that the Deep Learning approach is capable of providing better detection performance on complex attacks compared to conventional Machine Learning methods. This research is expected to serve as a reference in the development of adaptive and accurate network intrusion detection systems.

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