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All Journal KLIK: Kajian Ilmiah Informatika dan Komputer
Antika Zahrotul Kamalia
Universitas Pelita Bangsa, Bekasi
Computer Science & IT

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Penerapan Naïve Bayes Classifier, Support Vector Machine, dan Decision Tree untuk Meningkatkan Deteksi Ancaman Keamanan Jaringan Ahmad Zy; Ananto Tri Sasongko; Antika Zahrotul Kamalia
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1134

Abstract

This research aims to implement three machine learning algorithms, namely Naïve Bayes Classifier, Support Vector Machine (SVM), and Decision Tree, to enhance network security threat detection. The study utilizes data from multiple sources to train the machine learning models and evaluate their performance in detecting network security threats such as malware, ransomware, and spyware. The research results indicate that all three machine learning algorithms can improve the effectiveness of network security threat detection, surpassing conventional methods in terms of accuracy. Decision Tree yields the best results with a precision of 0.98, , followed by SVM with a precision of 90%, While Naïve Bayes Classifier a precision of 0.86.
Co-Authors Ahmad Zy Ananto Tri Sasongko