Bandingkan metode

	Log-Loss (Cross-Entropy Loss)	Akurasi
Bidang	Evaluasi Model	Evaluasi Model
Keluarga	MCDM	MCDM
Tahun asal	1990s	20th century
Pencetus	Information theory and machine learning literature	Historical statistical foundations
Tipe	Loss function	Evaluation metric
Sumber perintis	Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Alias	Cross-Entropy Loss, Logloss	Overall Accuracy, Correct Classification Rate
Terkait	3	5
Ringkasan	Log-loss measures the difference between predicted probabilities and actual labels, penalizing confident wrong predictions more than uncertain ones. It is a standard loss function in machine learning optimization and evaluates probabilistic classifier calibration.	Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.