PENGENALAN POLA WAJAH MENGGUNAKAN WEBCAM UNTUK ABSENSI DENGAN METODE LEARNING VECTOR QUANTIZATION(LVQ)
DOI:
https://doi.org/10.33143/jics.v10i1.4120Abstract
Sistem aplikasi pengenalan pola wajah merupakan suatu sistem yang dapat digunakan pada berbagai macam sistem pengawasan identifikasi dan keamanan salah satunya untuk sistem absensi. Sistem ini mengarah kepada sistem pendeteksian wajah secara otomatis. Pengenalan pola wajah dilakukan dengan cara mengidentifikasi dan menemukan lokasi citra wajah manusia. Tujuan penelitian adalah untuk membahas proses pengenalan pola wajah untuk absensi menggunakan algoritma learning vector quantization, data yang digunakan berupa input citra wajah sehingga proses pengindentifikasian wajah dapat dilakukan secara realtime, pengidentifikasi dihitung dengan kesamaan nilai bobot citra latih dengan dengan citra uji, dengan adanya sistem absensi wajah tersebut dapat membantu suatu instansi dalam menertibkan kedisiplinan kehadiran karyawannya, hasil yang ditampilkan dalam sistem ini beruba kepemilikan citra wajah untuk kategori absen pagi dan sore dengan range waktu yang telah di tentukan. Adapun tingkat keberhasilan yang telah diuji mendapatkan akurasi 80%.
Kata kunci: Pengenalan pola wajah, Learning Vector Quantization, RealtimeÂ
Facial pattern recognition application system is a system that can be used on a wide range of identification and security surveillance systems one for the attendance system. This system leads to automatic face detection system. Facial pattern recognition is done by identifying and finding the location of the image of a human face. The research objective in making this final task is to discuss the process of pattern recognition facial attendance algorithm using learning vector quantization, the data used in the form of input facial image so that the process identification  face can be done in realtime, identifiers calculated similarity weight value image practiced with the test images , with the face of the attendance system can help an agency to curb the presence discipline employees, the results displayed in this system about proprietary facial image to a category absent morning and afternoon with a time range that has been set. As for the level of success that has been tested to get an accuracy of 80%.
Keywords: Facial pattern recognition, Learning Vector Quantization, Realtime
References
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