PERBANDINGAN ALGORITMA RENDOM FOREST DAN SUPPORT VECTOR MECHINE (SVM) UNTUK PREDIKSI TREN PASAR CRYPTOCURRENCY BERBASIS WEBSITE
Abstract
Pasar cryptocurrency memiliki karakteristik yang sangat fluktuatif dan sulit diprediksi secara manual, sehingga dibutuhkan pendekatan berbasis machine learning untuk membantu dalam menganalisis tren harga. Penelitian ini bertujuan untuk membandingkan performa algoritma random forest dan support vector machine (SVM) dalam memprediksi tren pasar cryptocurrency, serta mengembangkan sistem prediksi berbasis website yang dapat diakses oleh pengguna. Penelitian menggunakan metode pengembangan perangkat lunak waterfall dan data historis bitcoin dan ethereum yang diperoleh dari CoinGecko. Proses pengolahan data mencakup pembersihan, pelabelan tren, serta pelatihan model dengan algoritma random forest dan SVM. Hasil pengujian menunjukkan bahwa kedua algoritma menghasilkan akurasi sangat tinggi, yaitu 100% untuk bitcoin dan 98,61% untuk ethereum. Sistem berhasil dikembangkan menggunakan framework flask dan Chart.js, dengan fitur pelatihan model, prediksi otomatis, dan visualisasi grafik harga. Kesimpulannya, algoritma yang digunakan sama-sama efektif dalam memprediksi tren pasar kripto, dan sistem yang dibangun dapat dijadikan alat bantu analisis bagi pengguna
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