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All Journal Jupiter JUITA : Jurnal Informatika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Ilmiah Matrik INTECOMS: Journal of Information Technology and Computer Science Journal of Information Systems and Informatics Journal of Information Technology Ampera Jurnal Pengabdian kepada Masyarakat Bina Darma Jurnal Jaringan Komputer dan Keamanan (JJKK)
Chairul Mukmin
Universitas Bina Darma
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Perbandingan Kinerja Server Virtual Pada Proses High Availability Chairul Mukmin
Jurnal Jaringan Komputer dan Keamanan Vol 1 No 1 (2020): Jurnal Jaringan Komputer dan Keamanan
Publisher : Institute of Information Technology and Social Science (IITSS)

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Abstract

This research is motivated by the complexity of each company being required to provide services without limits and can be accessed at any time, this must be supported by a reliable system to support so that the services provided can be accessed at any time. For this reason, it is necessary to analyze the virtual machine specifically for server repair when it is decided on the service that is running. This research discusses about improving server performance on virtual machines by using vmware and proxmox as virtual machines. This research uses the action research method. Testing is done by measuring the use of server resources when using virtual machines from the main server to the backup server. The results showed that VMware was more efficient in using server resources with high availability of 22.9% CPU, 1.73 GB of memory and 4.07 MB network while Proxmox had greater use of existing resources with high availability of CPU 28.8%, memory 2, 15GB and a 2.03MB network. From the data above shows that VMware has a more stable performance compared to Proxmox in terms of resource server usage when the process is high.
Machine Learning Models for DDoS Detection in Software-Defined Networking: A Comparative Analysis Ferdiansyah Ferdiansyah; Darius Antoni; Muhammad Valdo; Mikko Mikko; Chairul Mukmin; Usman Ependi
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.864

Abstract

In today's digital age, Software-Defined Networking (SDN) has become a pivotal technology that improves network control and flexibility. Despite its advantages, the centralized nature of SDN also makes it susceptible to threats such as Distributed Denial of Service (DDoS) attacks. This study compares the effectiveness of three machine learning models Random Forest, Naive Bayes, and Linear Support Vector Classification (LinearSVC) using the 'DDoS SDN dataset' from Kaggle, which contains 104,345 records and 23 features. An equal 70/30 ratio was used on model. The models were then assessed using measures such as accuracy, precision, recall, and F1-score, and ROC curves. Among the models, Random Forest outperformed the others with a 97% accuracy, precision values of 1.00 (benign traffic) and 0.94 (malicious traffic), and an ROC AUC score of 1.00. In contrast, Naive Bayes and LinearSVC recorded lower accuracies of 63% and 66%, respectively. These findings underscore Random Forest's effectiveness in detecting DDoS attacks within SDN environments.
Co-Authors Astrid Lestari Tungadi Darius Antoni Dwi Hurriyati Dwi Marisa Midyanti Edi Supratman Edi Surya Negara Erick Alfons Lisangan Ferdiansyah Ferdiansyah Fitri Purwaningtias Leon Andretti Abdillah Mikko Mikko Muhammad Valdo Naraloka, Therezia Qodri Harun Andriyanto Rizky Hardiyan Firmansyah Sholeha Deka Usman Ependi Widya Cholil Yuni Novita Sari