Implementasi Ekstrasi Fitur Dan K-Nearest Neightbor Untuk Identifikasi Wajah Personal

Pamungkas, Danar Putra; Setiawan, Ahmad Bagus

doi:10.30736/jti.v3i2.233

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…Dengan metode GLCM pengenalan jenis buah jeruk mencapai 91,6% [7]. Akurasi pengenalan wajah dengan menggunakan metode K-NN mencapai 80% [8]. Dalam proses pengelompokan metode KNN dapat menghasilkan akurasi sebesar 85,15% [9].…”

Section: Pendahuluanunclassified

Ekstraksi Citra menggunakan Metode GLCM dan KNN untuk Identifikasi Jenis Anggrek (Orchidaceae)

Pamungkas

2019

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Orchidaceae is the Latin name of an orchid that has various shapes, colors and sizes of flowers with unique features. The shape and color of the lips or labellum is a unique orchid that is a differentiator from other plants. In general, the types of orchids have similar colors, textures and flower petals, this causes a person to have difficulty identifying orchid species, especially people who do not know the characteristics of some types of orchids. Therefore the process of identifying orchids needs to be done automatically with a computer system so that it is expected to make it easier to identify the types of orchids. In this study using the GLCM method for feature extraction and KNN method for the process of identifying orchids or orchidaceae. Stages of identification of orchid images are changing the initial size of the image, conversion into gray degrees, median filters, feature extraction of the GLCM method and identification with the KNN method. The success rate of identifying Orchidaceae or orchids reaches 80% with an average of 77%. K value influences the success rate of identification, the greater the K value the smaller the accuracy.

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Section: Pendahuluanunclassified

Ekstraksi Citra menggunakan Metode GLCM dan KNN untuk Identifikasi Jenis Anggrek (Orchidaceae)

Pamungkas

2019

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“…Database utilization is (Pamungkas, 2017): 1) One of the important components in information systems, because it is the basis for providing information. 2) Determines the quality of information: accurate, timely and relevant.…”

Section: Introductionmentioning

confidence: 99%

Application of Enterprise Architecture to Decentralized Architecture in Internet Databases

Amanda,

Murti,

Nugroho

et al. 2023

JASa

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Enterprise architecture is a holistic, hierarchical and abstract description of the essential elements of an organization to maximize shareholder value over time. Enterprise Architecture (EA) is a way to structure enterprise information system elements which can be a set of models and relationships between enterprise elements used in planning, designing, and realizing an enterprise structure, business processes, information systems, and related infrastructure in inside. The database application architecture describes the basic design of the database application to be built. The database architecture describes the interaction diagram between the components that make up the database management system. These components include hardware devices, software, computer networks, and users. Distribution Architecture or Decentralized Architecture has its own advantages and disadvantages.

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Improved accuracy of recognizing of low-quality face images using two directional matrix in 2D-PCA algorithms and euclidean distance

Kuncoro

Sugiharti

2021

J. Phys.: Conf. Ser.

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Face recognition is a technique that can be used to distinguish the characteristic facial patterns of a person. A very influencing factor in the facial recognition process is image quality, so it can affect the level of accuracy. Improving the accuracy of low-quality facial recognition can be done by processing the image for its feature extraction using Two Directional Matrix on 2D-PCA. From the extraction process, the Eigenfaces value is then generated to be classified. Image classification is done by using Euclidean Distance. Furthermore, the accuracy results between image recognition with or without Two Directional Matrix will be compared. The data used were the AT&T face of database and database of Essex. Of the 18 tests carried out on the Two Directional Matrix method, it was proven to improve facial recognition accuracy by as many as 12 trials. In 5 other experiments the accuracy decreased and 1 experiment was unable to increase or decrease facial recognition accuracy. The highest accuracy result in the experiment using AT&T was 98.25%, while in Essex it was 97.27%. Suggestions for further research by conducting experiments with more diverse dataset ratios in order to get better low-quality image accuracy results.

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Implementasi Ekstrasi Fitur Dan K-Nearest Neightbor Untuk Identifikasi Wajah Personal

Cited by 3 publications

References 1 publication

Ekstraksi Citra menggunakan Metode GLCM dan KNN untuk Identifikasi Jenis Anggrek (Orchidaceae)

Ekstraksi Citra menggunakan Metode GLCM dan KNN untuk Identifikasi Jenis Anggrek (Orchidaceae)

Application of Enterprise Architecture to Decentralized Architecture in Internet Databases

Improved accuracy of recognizing of low-quality face images using two directional matrix in 2D-PCA algorithms and euclidean distance

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