Implementasi Face Recognition Menggunakan Algoritma K-Nearest Neighbors untuk Sistem Automasi Perpustakaan
DOI:
https://doi.org/10.29407/qxnr7g23Keywords:
Face Recognition, KNN, Perpustakaan, OpenCV, StreamlitAbstract
Perpustakaan merupakan sarana krusial dalam pendidikan, namun sistem konvensional seringkali terhambat oleh proses administrasi manual dan penggunaan kartu anggota fisik yang rentan hilang atau rusak. Penelitian ini bertujuan untuk mengatasi masalah efisiensi tersebut dengan mengimplementasikan sistem face recognition sebagai metode verifikasi identitas mahasiswa otomatis. Metode yang digunakan meliputi ekstraksi fitur wajah menggunakan pustaka OpenCV dan klasifikasi identitas menggunakan algoritma K-Nearest Neighbors (KNN) dengan parameter . Sistem ini diintegrasikan dengan basis data SQLite dan antarmuka berbasis web menggunakan framework Streamlit untuk memfasilitasi proses registrasi, peminjaman, dan pengembalian buku secara real-time. Hasil pengujian menunjukkan bahwa sistem mampu mencapai akurasi maksimal sebesar 100% pada kondisi cahaya terang dengan jarak ideal 30-50 cm, serta memiliki waktu respon identifikasi yang sangat cepat yaitu di bawah 2 detik. Rata-rata akurasi keseluruhan dari berbagai kondisi lingkungan adalah 86,25%. Simpulan dari penelitian ini adalah teknologi biometrik wajah dapat meningkatkan akurasi data transaksi dan mempercepat layanan sirkulasi di perpustakaan.
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