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      "abstract": "Keamanan jaringan pada ekosistem Internet of Things (IoT) menjadi isu\r\nkrusial seiring meningkatnya perangkat terhubung dengan sumber daya komputasi\r\nterbatas. Ancaman seperti penyadapan data dan serangan intrusi membahayakan\r\nintegritas sistem, sementara metode deteksi konvensional kerap membebani\r\nkinerja perangkat. Penelitian ini mengusulkan sistem keamanan bertingkat yang\r\nmenggabungkan kriptografi AES-128 untuk kerahasiaan data dan Intrusion\r\nDetection System (IDS) berbasis Long Short-Term Memory (LSTM) untuk deteksi\r\nanomali. Model LSTM dilatih menggunakan dataset CICIoT2023 dengan teknik\r\nSMOTE pada data latih untuk menyeimbangkan kelas (rasio awal\r\nnormal:serangan 1:41), kemudian dioptimasi menggunakan kuantisasi dinamis\r\nTensorFlow Lite (TFLite) agar efisien dijalankan di perangkat edge. Pengujian\r\ndilakukan dengan mensimulasikan enkripsi secara terisolasi pada VM 32-bit dan\r\npengujian integrasi inferensi pada lingkungan 64-bit. \r\nHasil penelitian menunjukkan model mampu mencapai akurasi\r\nkeseluruhan sebesar 99%, dengan kemampuan mengenali kelas normal yang\r\nsangat baik (Recall 0,99 dan F1-score 0,83). Konversi ke format TFLite berhasil\r\nmempercepat waktu inferensi rata-rata hingga 134,3 kali lipat (dari 113,85 ms\r\npada model standar menjadi 0,848 ms) tanpa mengorbankan performa deteksi.\r\nProses enkripsi AES-128 berjalan sangat efisien dengan latensi di bawah 1 ms.\r\nSecara akumulatif, integrasi sistem keamanan enkripsi dan deteksi anomali (endto-end)\r\n\r\nini hanya menambah total beban latensi sekitar 1,0 ms, yang sangat\r\nmemenuhi standar real-time untuk aplikasi IoT (umumnya < 100 ms). Penelitian\r\nini membuktikan bahwa kombinasi enkripsi simetris dan deteksi anomali berbasis\r\nkecerdasan buatan yang dioptimasi sangat layak diimplementasikan pada\r\nekosistem perangkat edge yang terbatas, meskipun implementasi langsung pada\r\nperangkat keras fisik masih disarankan untuk pengujian selanjutnya.",
      "lastmod": "2026-07-13 06:28:21",
      "keywords": "IoT, LSTM, TensorFlow Lite, Deteksi Anomali, Enkripsi AES-128, Anomaly Detection, AES-128 Encryption.",
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