ANALISIS KLASIFIKASI ARAH HARGA KRIPTO MENGGUNAKAN POHON KEPUTUSAN (DECISION TREE): PENGUJIAN KETERGANTUNGAN PADA FITUR LAGGED DAN INDIKATOR TEKNIS
DOI:
https://doi.org/10.69916/comtechno.v3i2.399Keywords:
data mining, pohon keputusan, kripto, prediksi harga, feature engineeringAbstract
Pasar aset kripto dicirikan oleh volatilitas tinggi, menjadikannya tantangan signifikan dalam prediksi pergerakan harga. Penelitian ini bertujuan untuk mengklasifikasikan arah pergerakan harga harian (Naik/Turun) dari data historis kripto ("btcidr.csv") menggunakan algoritma Decision Tree. Proses Data Mining melibatkan pembersihan data, rekayasa fitur melalui penciptaan lagged features dan indikator teknis seperti Moving Average (MA), serta pembentukan variabel target Klasifikasi. Model yang dilatih menunjukkan Akurasi sebesar 60% pada data uji. Hasil evaluasi mengungkap adanya ketidakseimbangan kinerja: model sangat baik dalam memprediksi kelas Turun (Recall 0.88), namun lemah dalam memprediksi kelas Naik (Recall 0.32). Analisis Feature Importance menegaskan bahwa harga penutup hari ini (Terakhir) dan harga hari sebelumnya (Terakhir_lag1) adalah prediktor dominan, sementara indikator teknis (MA_5 dan Perubahan%) diabaikan oleh model sederhana ini. Disarankan penggunaan teknik oversampling dan model ensambel untuk meningkatkan sensitivitas prediksi kenaikan harga.
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Copyright (c) 2025 Muhammad Fakhri Cholil, Akhlis Munazilin
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