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      "title": "PERBANDINGAN ALGORITMA LSTM, Bi-LSTM, DAN GRU UNTUK\r\nPREDIKSI HARGA CRYPTO",
      "abstract": "Tingginya Ketidakstabilan harga cryptocurrency seperti Bitcoin\r\nmenciptakan risiko signifikan bagi investor dan institusi keuangan. Model prediksi\r\ntradisional seringkali gagal menangkap pola data deret waktu yang kompleks dan\r\nnon-stasioner ini. Meskipun model deep learning seperti LSTM, Bi-LSTM, dan\r\nGRU telah banyak diterapkan, terdapat kebutuhan untuk perbandingan kinerja yang\r\nkomprehensif untuk menentukan arsitektur yang paling optimal dalam\r\nmemprediksi harga aset yang sangat fluktuatif. \r\nPenelitian ini membandingkan tiga model yaitu LSTM, Bi-LSTM, dan \r\nGRU. Metode penelitian dimulai dari pengumpulan data harga Bitcoin (BTC) per \r\njam periode 2018-2025. Data harga penutupan (Close) kemudian diproses\r\nmenggunakan normalisasi MinMax Scaler dan teknik sliding window dengan time\r\nstep 24 jam. Dataset dibagi menjadi 80% data latih dan 20% data uji, kemudian\r\nketiga model dievaluasi kinerjanya menggunakan metrik RMSE, MAE, MAPE, dan\r\nR2 Score. \r\nHasil penelitian menunjukkan bahwa model Bi-LSTM mencapai kinerja\r\nterbaik dengan tingkat error terendah dengan RMSE 503.49, MAE 331.89, MAPE\r\n0.39%, dan R² 0.9994. Model LSTM menempati posisi kedua dengan RMSE\r\n518.03, MAE 343.63, MAPE 0.41%, dan R² 0.9994, sedangkan GRU memiliki\r\nerror tertinggi dengan RMSE 618.01, MAE 441.79, MAPE 0.49%, dan R² 0.9991.\r\nMenariknya, Bi-LSTM juga terbukti paling efisien dalam pembelajaran (konvergen\r\ndi epoch 19) meskipun paling lambat secara komputasi (13ms\/step). Penelitian ini\r\nmemberikan kontribusi praktis bagi investor dan pengembang sistem FinTech\r\ndalam memilih model prediksi yang paling akurat dan efisien.",
      "dir": "disk0\/00\/03\/22\/52",
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      "institution": "Universitas AMIKOM Yogyakarta",
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        "000.000.006"
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