RANCANG BANGUN SISTEM ABSENSI KARYAWAN BERBASIS FACE ID DENGAN TEKNOLOGI HAAR CASCADE (STUDI KASUS: AUTOMEKANIKA BOSCH CAR SERVICE)
Abstract
Sistem absensi manual pada Automekanika Bosch Car Service menimbulkan permasalahan operasional berupa beban kerja berlebih bagi kepala divisi dalam pencatatan kehadiran dan tingginya risiko kesalahan input data jam kerja karyawan. Penelitian ini bertujuan merancang dan mengimplementasikan sistem absensi otomatis berbasis pengenalan wajah menggunakan teknologi Haar Cascade Classifier dan Local Binary Pattern Histogram (LBPH). Metodologi pengembangan menggunakan model Waterfall yang mencakup analisis kebutuhan, perancangan sistem dengan UML, implementasi menggunakan Python dengan library OpenCV, PHP untuk backend, serta MySQL sebagai basis data. Pengujian dilakukan melalui metode Black Box terhadap 25 skenario fungsional dan White Box terhadap 15 modul kritis. Hasil penelitian menunjukkan tiga temuan utama: pertama, sistem berhasil melakukan pencatatan jam kerja secara otomatis tanpa intervensi kepala divisi; kedua, implementasi validasi ganda dan algoritma Haar Cascade menghasilkan akurasi pengenalan wajah 90% pada jarak optimal hingga 150 cm yang meminimalkan kesalahan pencatatan; ketiga, pengujian komprehensif membuktikan sistem berjalan sesuai spesifikasi dengan fitur rekapitulasi harian dan mingguan yang menyediakan informasi terstruktur mengenai jam kerja, jam lembur, dan status kehadiran. Implementasi sistem ini memberikan kontribusi dalam transformasi digital pencatatan kehadiran pada lingkungan bengkel otomotif.
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