Perbandingan Algoritma k-Nearest Neighbors (k-NN) dan Support Vector Machines (SVM) untuk Klasifikasi Pengenalan Citra Wajah

Parasian DP Silitonga, Romanus Damanik

Abstract

Abstract- The study of face recognition is one of the areas of computer vision that requires significant research at the moment. Numerous researchers have conducted studies on facial image recognition using a variety of techniques or methods to achieve the highest level of accuracy possible when recognizing a person's face from existing images. However, recognizing the image of a human face is not easy for a computer. As a result, several approaches were taken to resolve this issue. This study compares two (two) machine learning algorithms for facial image recognition to determine which algorithm has the highest level of accuracy, precision, recall, and AUC. The comparison is carried out in the following steps: image acquisition, preprocessing, feature extraction, face classification, training, and testing. Based on the stages and experiments conducted on public image datasets, it is concluded that the SVM algorithm, on average, has a higher level of accuracy, precision, and recall than the k-NN algorithm when the dataset proportion is 90:10. While the k-NN algorithm has the highest similarity in terms of accuracy, precision, and recall at 80%: 20% and 70%: 30% of 99.20. However, for the highest AUC percentage level, the k-NN algorithm outperforms SVM at a dataset proportion of 80%: 20% at 100%.

Keywords

Citra, Klasifikasi, SVM, k-NN, Akurasi, Precission, Recall

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References

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