Analisis Sistem E-Learning Berbasis ISO 27005:2018 Menggunakan Algoritma Klasifikasi Random Forest
DOI:
https://doi.org/10.34010/jati.v15i2.16173Keywords:
ISO 27005:2018, Keamanan Siber, Random ForestAbstract
Peningkatan penggunaan sistem e-learning dalam dunia pendidikan memberikan kemudahan akses dan fleksibilitas, tetapi juga menimbulkan risiko baru terkait keamanan informasi digital. Ketergantungan pada sistem digital menjadikan e-learning rentan terhadap serangan siber, seperti phishing, malware, insider threat, dan DoS/DDoS, yang dapat mengancam kerahasiaan, integritas, dan ketersediaan data. Penelitian ini bertujuan untuk mengidentifikasi dan mengevaluasi risiko keamanan siber pada sistem e-learning dengan mengintegrasikan manajemen risiko berbasis ISO 27005:2018 dan algoritma klasifikasi Random Forest. Data yang digunakan adalah English Classroom Security Threats Dataset yang berisi 10.000 data insiden simulasi ancaman siber pada lingkungan kelas digital. Metode penelitian meliputi analisis distribusi jenis ancaman, analisis korelasi antar fitur, visualisasi data, serta pelatihan model klasifikasi untuk mendeteksi jenis ancaman. Hasil penelitian menunjukkan bahwa phishing merupakan ancaman terbanyak (35%), diikuti malware (34%), insider threat (15%), social engineering (11%), dan DoS/DDoS (5%). Model Random Forest yang dibangun mampu mencapai akurasi prediksi 100% pada data uji, dengan Response_Time sebagai fitur paling berpengaruh (35%), diikuti Access_Attempt (22%). Temuan ini membuktikan bahwa integrasi ISO 27005:2018 dan machine learning efektif dalam mendukung identifikasi ancaman dan perancangan kebijakan mitigasi risiko pada sistem e-learning.
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