Analisis dan pengembangan sistem presensi pengenalan wajah menggunakan algoritma local binary pattern histogram pada karyawan PT Iplug Indonesia

Gunady, Ramiro (2022) Analisis dan pengembangan sistem presensi pengenalan wajah menggunakan algoritma local binary pattern histogram pada karyawan PT Iplug Indonesia. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

The presence of employees is an administrative evidence whose implementations always monitored by the institution because it is related to calculation of employees salaries. PT Iplug Indonesia uses a fingerprint attendance machine ICON 16 which can accommodate a capacity of 500 employees and 100.000 transaction logs. The weakness of fingerprint attendance machine is that it can’t identify dirty or injured fingerprints. Fingerprint attendance machine at PT Iplug Indonesia has experienced a memory full because memory installed on the machine is 1GB. To overcome the misidentification and the lack of memory from previous attendance machine, face recognition presence system was made using Local Binary Pattern Histogram algorithm After tested on 8 employees, it is concluded that the proposed method has ability to identify all the employees correctly and obtained the highest accuracy value is 92% and the lowest accuracy value is 64%./ Kehadiran atau presensi karyawan merupakan alat bukti administratif yang pelaksanaannya selalu dipantau oleh lembaga karena berkaitan dengan perhitungan pendapatan atau gaji para pegawai. PT Iplug Indonesia menggunakan alat presensi pengenalan sidik jari tipe ICON 16 yang mampu menampung kapasitas 500 karyawan dan 100.000 log transaksi. Kelemahan pada alat presensi pengenal sidik jari adalah tidak dapat mengidentifikasi sidik jari yang kotor atau terluka. Alat presensi pengenal sidik jari pada PT Iplug Indonesia pernah mengalami kepenuhan memori karena memori yang terpasang pada alat sebesar 1GB. Untuk mengatasi kesulitan identifikasi dan terbatasnya memori dari sistem presensi sebelumnya, maka dibuatlah sistem presensi pengenalan wajah menggunakan algoritma Local Binary Pattern Histogram. Setelah dilakukan pengujian pada 8 karyawan, mampu mengidentifikasi wajah karyawan dengan benar semua dan diperoleh nilai akurasi tertinggi adalah 92% dan nilai akurasi terendah adalah 64%.

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