MULTICLASS CLASSIFICATION OF SOUND HEALING WITH K-NEAREST NEIGHBOR ALGORITHM - Ramadhani, Cipta; Suksmadana, I Made Budi; Ariessaputra, Suthami; Suta Wijaya, I Gede Pasek (DIELEKTRIKA, 2021)
Sound healing can be described to the practice of sound vibrations in an individual body directly to bring about a state of harmony and healing. In many ancient country, sound healing was used as a part of medicine and healing ritual. In this paper, we propose K-Nearest Neighbors (KNN) method to categorize the type of sound healing. Acoustic Sound for Wellbeing (ASW) such as Drums, Gongs, Chimes and Singing Bowls are used as dataset for KNN algorithm. The KNN algorithm is applied to classify The ASW dataset in multi class classification tasks. In our model, KNN gave the best performance measurement for 2 Classes classification. the value of Accuracy, Precision and recall are higher than 0,87. Meanwhile, The confusion matrix for 3 classes presented the lowest point from all experimental setting. Furthermore, confusion matrix for 4 classes showed some anomaly.