Pengenalan Wajah Menggunakan SVM Multi Kernel dengan Pembelajaran yang Bertambah

Athoillah, Muhammad

doi:10.15575/join.v2i2.109

Cited by 13 publications

(8 citation statements)

References 5 publications

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“…This maximum distance is obtained by finding the best hyperplane (separating line) in the input space acquired by measuring the hyperplane margin. Margin is a distance between hyperplane and the nearest vector from each class [17].…”

Section: Algorithm Of Students' Facial Classificationmentioning

confidence: 99%

Classification of Facial Expression Using Principal Component Analysis (PCA) Method and Support Vector Machine (SVM)

Setiawati¹,

Sela²

2022

IJCIT

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Classification is a process to assert an object into one of defined categories. This study examines the classification of recognition of student’s facial expression during digital learning –indifferent and serious expression. The dataset used was from a vocational school -SMK Muhammadiyah 2 Bantul. This study used the combination of algorithm: Principal Component Analysis (PCA) and Support Vector Machine (SVM) to increase the accuracy. This study aims at comparing the performance of combination of two algorithm: (PCA to SVM) and (PCA to k-NN). The result states that the combination of PCA-SVM algorithm is higher than the combination of PCA-k-NN algorithm with the average accuracy of 96% and 89%.

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Section: Algorithm Of Students' Facial Classificationmentioning

confidence: 99%

Classification of Facial Expression Using Principal Component Analysis (PCA) Method and Support Vector Machine (SVM)

Setiawati¹,

Sela²

2022

IJCIT

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“…This research will utilize the Support Vector Machine (SVM) method in conducting data training and testing. SVM is a technique for finding hyperplanes that can separate two data sets from two different classes [4]. There are also advantages of SVM, namely being able to determine distances using support vectors so that the computation process becomes faster and more effective.…”

Section: Svm (Support Vector Machinementioning

confidence: 99%

Design of Someone's Character Identification Based on Handwriting Patterns Using Support Vector Machine

Kumalasanti

2022

Int.J.Appl.Sci.Smart Technol.

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Image processing has a fairly broad scope and is rich in innovation. Today, image processing has developed with various reliable methods in almost all aspects of life. One of the uses of technology in the field of image processing is biometric identification. Biometric is a system that utilizes specific data in the form of individual physical characters in the process of identifying and validating data. There is also a biometric attribute that will be developed in this study is handwriting. The handwriting pattern of each individual has a different character and uniqueness so that it can be used as an identity. The uniqueness of this handwriting will be studied with the aim of recognizing a person's character or personality. If someone's personality data has been obtained, this can help the process of recruiting prospective employees in a company by simply reading from handwriting patterns. Handwriting can be studied by combining the science of Psychology so that it can provide output in the form of a person's characteristics or personality. This research will be developed using the multi class Support Vector Machine (SVM) classification. The preprocessing stage in the form of binarization, thinning and data extraction will also greatly affect the reliability of the system. Simulations with variations of variables and parameters are expected to obtain optimal accuracy.

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“…Untuk membandingkan nilai-nilai wajah digunakan algoritma klasifikasi Support Vector Machine (SVM). Support Vector Machine (SVM) merupakan metode klasifikasi yang diperkenalkan pertama kali oleh Vapnik pada tahun 1998 [11].…”

Section: Classificationunclassified

Implementasi Deep Learning Menggunakan CNN Untuk Sistem Pengenalan Wajah

Dewi

Ismawan

2021

FaktorExacta

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Face recognition system is generally divided into two stages, face detection system, which is a pre-processing step followed by a facial recognition system. This step will quickly be done by humans but it takes a long time for the computer. This ability of humans is what researchers want to duplicate in the last few years as biometric technology in computer vision to create a model of face recognition in computer. Deep learning becomes a spotlight in developing machine learning, the reason because deep learning has reached an extraordinary result in computer vision. Based on that, the author came up with an idea to create a face recognition system by implementing deep learning using the CNN method and applying library openFace. The CNN methods are still superior and widely used because they have good accuracy. The initial process was taking a picture of the face to be used as a dataset. From this dataset, face preprocessing will be carried out, that is, to extract the facial vector features into 128-d and to classify the facial vector. The contribution of this research is the addition of features to improve the accuracy of the facial recognition system using the CNN method. The results of this research get a precision value of 98.4%, a recall of 98% and an accuracy of 99.84%.

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Pengenalan Wajah Menggunakan SVM Multi Kernel dengan Pembelajaran yang Bertambah

Cited by 13 publications

References 5 publications

Classification of Facial Expression Using Principal Component Analysis (PCA) Method and Support Vector Machine (SVM)

Classification of Facial Expression Using Principal Component Analysis (PCA) Method and Support Vector Machine (SVM)

Design of Someone's Character Identification Based on Handwriting Patterns Using Support Vector Machine

Implementasi Deep Learning Menggunakan CNN Untuk Sistem Pengenalan Wajah

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