Perbandingan Algoritma KNN dan SVM Dalam Memprediksi Metaverse Crypto Token
https://doi.org/10.26594/teknologi.v15i1.5066
Abstract
Perubahan harga cryptocurrency cenderung fluktuatif, sehingga diperlukan teknik khusus untuk memprediksi harga cryptocurrency tersebut. Teknik prediksi data mining dengan metode regresi digunakan untuk membangun model prediksi harga cryptocurrency tersebut. Algoritma yang digunakan untuk pemodelan yaitu K-Nearest Neighbors (KNN) dan Support Vector Machine (SVM), kemudian hasil dari kedua algoritma tersebut dibandingkan untuk mendapatkan pemodelan yang lebih baik. Beberapa variabel yang digunakan pada data yaitu variabel independen seperti open, high, dan low serta variabel dependen seperti close. Evaluasi terhadap pengujian model dilakukan menggunakan berbagai teknik seperti menggunakan data murni, remove outlier, pembagian data mulai dari 70:30, 80:20, dan 90:10, serta standarisasi. Dari hasil evaluasi didapatkan bahwa algoritma SVM dengan teknik standarisasi z-score, dan pembagian data 70:30 memberikan nilai error paling kecil yaitu 5% serta dan nilai R2 Square yaitu 0,98.
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