DETEKSI POLA CANDLESTICK MENGGUNAKAN YOLOV8 UNTUK ANALISIS TEKNIKAL BERBASIS CITRA
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Abstract
Penelitian ini mengevaluasi kinerja model YOLOv8 dalam mendeteksi pola candlestick secara otomatis dari citra grafik keuangan. Pendekatan kuantitatif eksperimental digunakan dengan dataset 4.435 citra candlestick yang telah dianotasi dari Roboflow Universe. Model dilatih menggunakan konfigurasi standar YOLOv8 dengan learning rate 0.01, batch size 16, dan 100 epoch. Metrik evaluasi meliputi precision, recall, F1-score, dan mean Average Precision (mAP) pada rentang IoU [0.5:0.95]. Hasil menunjukkan YOLOv8 mencapai akurasi deteksi tinggi dengan precision 0.877, recall 0.898, dan mAP@[0.5:0.95] sebesar 0.903. Model menunjukkan kinerja kuat pada pola dengan ciri visual jelas seperti Bullish Engulfing dan Three Line Strike. Namun, tantangan sistematis muncul dalam membedakan pola dengan kemiripan visual tinggi, khususnya Morning Star–Morning Doji Star dan Evening Star–Evening Doji Star. Confusion matrix ternormalisasi mengungkapkan , model juga mampu membedakan pola yang mirip dengan hasil tertinggi 78% pada pola morning star dan nilai terendah 46% pada pola evening doji star, menunjukkan ambiguitas visual yang melekat pada representasi candlestick. Penelitian ini memberikan benchmark komprehensif untuk YOLOv8 dalam deteksi objek finansial dan menekankan perlunya peningkatan fitur berbasis konteks semantik untuk pasangan pola yang membingungkan.
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