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      "abstract": "Perkembangan teknik obfuscation pada malware modern telah menjadi tantangan\r\nsignifikan bagi sistem keamanan siber, di mana malware mampu menyamarkan\r\nkode berbahaya untuk menghindari deteksi antivirus tradisional berbasis signature.\r\nOleh karena itu, pendekatan analisis dinamis melalui forensik memori menjadi\r\nsolusi alternatif yang krusial. Penelitian ini bertujuan untuk mengkomparasi kinerja\r\ndua algoritma Supervised Machine Learning, yaitu Support Vector Machine (SVM)\r\ndan K-Nearest Neighbor (KNN), dalam mendeteksi obfuscated malware. Dataset\r\nyang digunakan adalah CIC-MalMem-2022 dan MalwareMemoryDump  yang\r\nberisi fitur-fitur ekstraksi memori dari berbagai jenis malware (Spyware,\r\nRansomware, Trojan) dan aplikasi benigna. Metodologi penelitian meliputi tahap\r\npreprocessing data, normalisasi fitur menggunakan Standard Scaler, dan pelatihan\r\nmodel dengan pembagian data latih dan uji sebesar 80:20. Hasil pengujian\r\nmenunjukkan bahwa algoritma [Model Terbaik, misal: SVM] memiliki performa\r\nyang lebih unggul dengan akurasi mencapai [...%], dibandingkan algoritma [Model\r\nLain] yang memperoleh akurasi [...%]. Namun, dari segi waktu komputasi,\r\nalgoritma [Model Tercepat] terbukti lebih efisien. Penelitian ini menyimpulkan\r\nbahwa metode Machine Learning berbasis memori efektif dalam mengidentifikasi\r\nmalware yang tersamar, dengan [Model Terbaik] sebagai rekomendasi utama untuk\r\ndeteksi yang memprioritaskan akurasi.",
      "lastmod": "2026-09-16 03:03:19",
      "date": "2026-02-20",
      "subjects": [
        "000.000.005"
      ],
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      "eprintid": 32788,
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