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Dogecoin merupakan salah satu aset cryptocurrency yang awalnya dibuat sebagai parodi namun berkembang menjadi aset digital dengan kapitalisasi pasar yang besar. Transaksi dan investasi menggunakan Dogecoin memiliki tingkat volatilitas yang tinggi sehingga harga sering berubah secara signifikan dalam waktu singkat. Oleh karena itu, diperlukan prediksi harga yang akurat untuk membantu pengambilan keputusan investasi. Penelitian ini bertujuan untuk membandingkan tiga algoritma machine learning linear, yaitu Support Vector Regression (SVR) dengan Kernel Linear, Regresi Linear, dan AdaBoost dalam memprediksi harga Dogecoin untuk satu hari ke depan. Dataset yang digunakan merupakan data historis harga harian Dogecoin dari tahun 2019 hingga 2025 yang diperoleh dari Investing.com, meliputi harga Open, High, Low, dan Close. Proses prapemrosesan data dilakukan menggunakan Z-Score Normalization, sedangkan pembagian data dilakukan dalam dua skenario, yaitu 80% pelatihan 20% pengujian dan 60% pelatihan 40% pengujian. Hasil penelitian menunjukkan bahwa model SVR Kernel Linear memiliki performa terbaik dengan MAE 0.00471 USD, RMSE 0.01355 USD, dan R² 0.98032, mengungguli Regresi Linear dan jauh lebih baik dibandingkan AdaBoost. Berdasarkan hasil evaluasi, SVR Kernel Linear terbukti sebagai model paling akurat dan stabil dalam memprediksi harga Dogecoin karena mampu menangkap pola hubungan linear antar variabel secara efektif.
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