KLASIFIKASI TRAFFIC JARINGAN ONLINE GAMBLING BERBASIS DEEP LEARNING

PRASISKA, RAHAYU and Stiawan, Deris and Afifah, Nurul (2025) KLASIFIKASI TRAFFIC JARINGAN ONLINE GAMBLING BERBASIS DEEP LEARNING. Undergraduate thesis, Sriwijaya University.

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Abstract

Pesatnya pertumbuhan aktivitas judi online menimbulkan kebutuhan akan sistem deteksi yang akurat dan efisien. Penelitian ini bertujuan untuk mendeteksi aktivitas judi online dalam lalu lintas jaringan menggunakan kombinasi Autoencoder dan Deep Neural Network (DNN). Dataset diperoleh dari data lalu lintas jaringan dalam format PCAP yang kemudian diekstraksi menggunakan Tshark dan dikonversi ke format CSV. Proses dimulai dengan ekstraksi fitur melalui Autoencoder untuk menghasilkan representasi data berdimensi rendah, dilanjutkan dengan klasifikasi menggunakan model DNN. Evaluasi dilakukan dengan tiga skenario proporsi data (70:15:15, 80:10:10, dan 90:5:5) dan berbagai variasi parameter pelatihan. Hasil terbaik diperoleh pada proporsi data 70:15:15 dengan varian model DNN keempat pada epoch ke-200, yang mencapai F1-score sebesar 99,38% dan akurasi 99,87%. Model menunjukkan stabilitas performa tanpa indikasi overfitting, serta hasil evaluasi yang lebih representatif dibanding proporsi data lainnya. Penelitian ini menunjukkan bahwa pendekatan Autoencoder-DNN mengidentifikasi traffic judi online secara otomatis dan andal.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Keamanan Jaringan, Traffic Network, Deep Neural Network, Autoencoder
Subjects:	T Technology > T Technology (General) > T57.6-57.97 Operations research. Systems analysis > T57.85 Network systems theory Including network analysis Cf. TS157.5+ Scheduling
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Rahayu Prasiska
Date Deposited:	04 Aug 2025 07:53
Last Modified:	04 Aug 2025 07:53
URI:	http://repository.unsri.ac.id/id/eprint/182224

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