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IMPLEMENTASI GRAY LEVEL CO-OCCURRENCE MATRIX UNTUK PENGENALAN CITRA WAJAH

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Penilaian

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Biometric recognition can be done in various ways, one of which is facial recognition. The face recognition process sometimes still fails, among these failures are caused by lighting factors, object-to-tool distance, object-to-tool angle, facial expression and position. It takes a facial recognition method that is able to give the best results. Many methods have been introduced by scientists and researchers for facial recognition. One of these methods is the Gray Level Co-Occurrence Matrix (GLCM) feature extraction method. The GLCM method is used for feature extraction of facial image data. The resulting feature data is then classified using the K-Nearest Neighbor (KNN) algorithm. This study uses data totaling 160 facial images with 4 test data formations, namely 150 training data and 10 test data, 100 training data and 10 test data, 90 training data and 10 test data, as well as 80 training data and 10 test data. The test results get the highest accuracy of 70%, average precision of 63%, and average recall of 70% in tests with 90 training data and 10 test data. The author concludes that the GLCM extraction method and the KNN algorithm are quite good in recognize faces in the dataset used.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207002646	T75910	T759102022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T759102022

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

iv, 54 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

003.107

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Jurusan Teknik Informatika
Identifikasi Sistem

Specific Detail Info

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Statement of Responsibility

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