Face Identification Based on K-Nearest Neighbor

Wirdiani, Ni Kadek Ayu; Hridayami, Praba; Widiari, Ni Putu Ayu; Rismawan, Komang Diva; Candradinata, Putu Bagus; Jayantha, I Putu Deva

doi:10.15294/sji.v6i2.19503

Cited by 29 publications

(21 citation statements)

References 7 publications

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“…KNN can give results with great accuracy, according to a number of earlier research. For instance, research on face categorization using KNN yields an accuracy of 81 percent when k = 1 (Wirdiani et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Drug Type Classification Performance Using KNN Algorithm

Aldi

Nozomi

Soeheri³

2022

SinkrOn

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The error of decommissioning is a serious problem that is often faced in medicine. In the face of these problems, information technology has a very important role. One of the information technologies that can be used is to use the machine learning classification algorithm K-Nearest Neighbor KNN. KNN is a type of machine learning algorithm that can be applied to problems with classification and regression prediction. The classification of types of drugs for patients greatly affects the health of the patient. The patient data is processed and transformed to numbers, which are then divided into training data and test data from 90:10, 80:20, 70:30 and using the Cross Validation model. KNN works through the nearest neighboring value with a value of k = 3 calculated by the calculation of Euclidean Distance, and then evaluated using the Confusion Matrix. The performance of the KNN algorithm resulted in the highest Accuracy value of 98.33%, a Precision value of 98.8%, a Recall value of 96.2%, and an F-measure value of 97.48%. The performance is obtained from the sharing of training data and 90:10 test data. The data share results in high performance compared to other data shares, including using the Cross Validation model. And the lower the k value, the higher the value of the resulting performance. The results show that the performance of the KNN algorithm is working well.

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Section: Literature Reviewmentioning

confidence: 99%

Comparison of Drug Type Classification Performance Using KNN Algorithm

Aldi

Nozomi

Soeheri³

2022

SinkrOn

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“…Several previous studies have shown that KNN can produce high accuracy. Such as research on face classification with KNN which produces an accuracy of 81% with k = 1 (Wirdiani et al, 2019). Furthermore, research on the classification of tomato maturity using K-Nearest Neighbor which produces the highest level of accuracy reaches 92.5% with the k parameter used as many as 3 (Sanjaya, Pura, Gusti, Yanto, & Syafria, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Broadleaf Weeds Using a Combination of K-Nearest Neighbor (KNN) and Principal Component Analysis (PCA)

Sinlae

Alamsyah

Suhery

et al. 2022

SinkrOn

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Palm oil is one of the leading commodities in Indonesia. Oil palm yields can be influenced by several factors, one of which is proper weed control. Uncontrolled weeds can damage oil palm plantations. To be able to manage and control weeds, especially large leaf weeds, it is necessary to know the types of weeds. However, not all farmers have knowledge about the types of weeds. For that we need a system that can help identify broadleaf weeds based on leaf images using image processing. So this study aims to build a large leaf weed classification system using a combination of the K-Nearest Neighbor (KNN) and Principal Component Analysis (PCA) algorithms. PCA is used as feature extraction based on the characteristics formed from each spatial property. PCA can be used to reduce and retain most of the relevant information from the original features according to the optimal criteria. The results of the information will then be used by KNN for learning by paying attention to the closest distance from the object. Based on the test results, the developed model is able to produce an accuracy of 90%. Principal Component Analysis (PCA) and K-Nearest Neighbor (KNN) algorithms can be used in the classification process properly. Accuracy results are strongly influenced by the amount of training data and test data as well as the quality of the image used.

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“…One of the simple algorithms that can be used in the classification process to match data between testing and training data from face datasets is KNN [9]. KNN was used in the early 1970s for statistical estimation and pattern recognition [22].…”

Section: Classification With Knn Algorithmmentioning

confidence: 99%

“…Classification using KNN can be done after feature extraction to identify face. As in Wirdiani et al [9] carried out three stages to identify face including face detection, feature extraction and classification. The method used to extract features is principal component analysis (PCA) and the method to perform classification is k-nearest neighbor (KNN).…”

Section: Introductionmentioning

confidence: 99%

Cloud-based architecture for face identification with deep learning using convolutional neural network

Herlambang

Buana

Piarsa

2021

IJEECS

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The use of a face as a biometric to identify a person in order to keep the system safe from an unauthorized person has advantages over other biometric characteristics. The face as a biometric has more structure and a wider area than other biometrics, while can be retrieved in a non-invasive manner. We proposed a cloud-based architecture for face identification with deep learning using convolutional neural network. Face identification in this study used a cloud-based engine with four stages, namely face detection with histogram of oriented gradients (HOG), image enhancement, feature extraction using convolutional neural network, and classification using k-nearest neighbor (KNN), SVM, as well as random forest algorithm. This study conducted a classification experiment with cloud-based architecture using three different datasets, namely Faces94, Faces96 and University of Manchester Institute of Science and Technology (UMIST) face dataset. The results from this study are with the proposed cloud-based architecture, the best accuracy is obtained by KNN algorithm with an accuracy of 99% on Faces94 dataset, 99% accuracy on Faces96 dataset, 97% on UMIST face dataset, and performance of the three algorithms decreased in UMIST face dataset with facial variations from various angles from left to right profile.

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Face Identification Based on K-Nearest Neighbor

Cited by 29 publications

References 7 publications

Comparison of Drug Type Classification Performance Using KNN Algorithm

Comparison of Drug Type Classification Performance Using KNN Algorithm

Classification of Broadleaf Weeds Using a Combination of K-Nearest Neighbor (KNN) and Principal Component Analysis (PCA)

Cloud-based architecture for face identification with deep learning using convolutional neural network

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