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      "abstract": "Prediksi harga cryptocurrency, khususnya Binance Coin (BNB), menjadi \r\ntantangan karena sifat data time series yang volatil dan dipengaruhi banyak faktor.\r\nKetidakakuratan prediksi dapat berdampak pada pengambilan keputusan investasi\r\nyang kurang optimal bagi investor maupun pelaku pasar. Oleh karena itu,\r\ndiperlukan model prediksi yang mampu menangkap pola historis secara lebih baik\r\nagar menghasilkan estimasi harga yang lebih akurat dan dapat diandalkan.\r\n \r\nPenelitian ini menggunakan metode Bidirectional Long Short-Term \r\nMemory (BiLSTM) untuk memodelkan data historis harga BNB. Tahapan\r\npenelitian meliputi pengumpulan data, preprocessing, pembentukan sequence\r\nmenggunakan teknik sliding window, serta pelatihan model. Untuk meningkatkan\r\nperforma, dilakukan optimasi hyperparameter menggunakan metode Random\r\nSearch. Model yang dihasilkan kemudian dievaluasi menggunakan metrik MAE,\r\nMAPE, dan R² untuk mengukur tingkat akurasi dan kemampuan generalisasi\r\nmodel.\r\n \r\nHasil penelitian menunjukkan bahwa model BiLSTM yang telah dioptimasi \r\nmemiliki performa lebih baik dibandingkan model baseline, dengan penurunan\r\nMAE dari 50,22 menjadi 20,73, MAPE dari 5,38% menjadi 2,35%, serta\r\npeningkatan R² dari 0,8694 menjadi 0,9713. Model juga berhasil\r\ndiimplementasikan dalam aplikasi berbasis web menggunakan Streamlit untuk\r\nprediksi harga dalam horizon 3, 7, 14 dan 30 hari. Penelitian ini berkontribusi dalam\r\nmenyediakan solusi prediksi yang akurat dan aplikatif, yang dapat dimanfaatkan\r\noleh investor, analis, dan peneliti. Pengembangan selanjutnya dapat mencakup\r\npenambahan variabel eksternal untuk meningkatkan akurasi model.",
      "thesis_name": "skripsi",
      "department": "Fakultas Ilmu Komputer",
      "eprint_status": "archive",
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