KOMPARASI ALGORITMA DECISION TREE DAN RANDOM FOREST DALAM MENDETEKSI INTRUSI JARINGAN INTERNET

KOMPARASI ALGORITMA DECISION TREE DAN RANDOM FOREST DALAM MENDETEKSI INTRUSI JARINGAN INTERNET

• By Farhan Maulana,Aqshal;
• S1 - Teknik Informatika
• 9
• 1-98

Keamanan jaringan merupakan aspek krusial dalam dunia digital saat ini, terutama dengan meningkatnya frekuensi dan kompleksitas serangan siber. Intrusion Detection System (IDS) menjadi salah satu alat penting untuk mengidentifikasi dan merespons serangan terhadap jaringan komputer. Penelitian ini bertujuan untuk membandingkan kinerja dua algoritma machine learning, Decision Tree dan Random Forest, dalam mendeteksi intrusi jaringan internet dengan menggunakan dataset NSL-KDD, versi yang lebih baik dari KDD 99. Dalam penelitian ini, kedua algoritma diterapkan pada dataset NSL-KDD yang berisi 125.973 sampel trafik jaringan, menggunakan pustaka scikit-learn dan bahasa pemrograman Python. Kinerja masing-masing algoritma dievaluasi berdasarkan akurasi deteksi intrusi. Hasil penelitian menunjukkan bahwa algoritma Decision Tree mencapai akurasi sebesar 93%, sementara algoritma Random Forest mencapai akurasi yang sangat baik sebesar 99%. Penelitian ini menyimpulkan bahwa meskipun kedua algoritma menunjukkan kinerja yang baik, Random Forest lebih unggul dalam hal akurasi deteksi intrusi pada dataset NSL-KDD. Temuan ini diharapkan dapat memberikan kontribusi signifikan dalam pengembangan sistem keamanan jaringan yang lebih efektif dan efisien, serta memberikan wawasan bagi penelitian lebih lanjut dalam bidang deteksi intrusi menggunakan teknologi machine learning.

• Katakunci
• URL https://drive.google.com/file/d/1E8LFUG7OOajMkiiZKod5twr-6V3FYq0P/view?usp=sharing
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