Perbandingan Algoritma Klasifikasi untuk Deteksi Intrusi pada Jaringan Komputer (Literature Review)
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Abstract
Dalam era digital yang semakin maju, keamanan jaringan komputer menjadi semakin krusial. Sistem Deteksi Intrusi (IDS) berperan penting dalam mengidentifikasi dan mencegah serangan siber. Studi ini melakukan tinjauan literatur untuk membandingkan berbagai algoritma klasifikasi yang digunakan dalam deteksi intrusi pada jaringan komputer, termasuk Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes, dan Neural Networks. Hasil menunjukkan bahwa Neural Networks dan Random Forest memiliki akurasi tinggi tetapi memerlukan sumber daya komputasi yang besar. Sebaliknya, Naive Bayes dan Decision Tree menawarkan kecepatan dan efisiensi komputasi yang lebih baik. Kesimpulan ini memberikan panduan bagi peneliti dan praktisi dalam memilih algoritma yang sesuai berdasarkan kebutuhan spesifik aplikasi dan karakteristik data yang digunakan.
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