FACE RECOGNITION MENGGUNAKAN ALGORITMA LOCAL BINARY PATTERN HISTOGRAM (LBPH) PADA SISTEM ABSENSI MAHASISWA
ADI EKO LAKSONO, BHIMA (2024) FACE RECOGNITION MENGGUNAKAN ALGORITMA LOCAL BINARY PATTERN HISTOGRAM (LBPH) PADA SISTEM ABSENSI MAHASISWA. Skripsi (S1) thesis, Universitas Muhammadiyah Ponorogo.
1. SURAT PERSETUJUAN UNGGAH KARYA ILMIAH.pdf
Download (258kB)
2. HALAMAN DEPAN.pdf
Download (2MB)
3. BAB I.pdf
Download (244kB)
4. BAB II.pdf
Restricted to Repository staff only
Download (200kB)
5. BAB III.pdf
Restricted to Repository staff only
Download (1MB)
6. BAB IV.pdf
Restricted to Repository staff only
Download (1MB)
7. BAB V.pdf
Restricted to Repository staff only
Download (208kB)
8. DAFTAR PUSTAKA.pdf
Download (284kB)
9. LAMPIRAN.pdf
Restricted to Repository staff only
Download (235kB)
10. SKRIPSI FULL TEXT_Bhima adi eko laksono_19533190_FACE RECOGNITION MENGGUNAKAN ALGORITMA LOCAL BINARY PATTERN HISTOGRAM.pdf
Restricted to Repository staff only
Download (4MB)
Abstract
Muhammadiyah University of Ponorogo, as one of the institutions higher education that has taken the initiative in implementing technology information to improve the efficiency and effectiveness of academic and operational operations administratively. The attendance system is an integral part of management academic and administrative that supports the smooth learning process. In this context, SIMTIK (Information Technology Management Information System and Communications) have played a key role in automating and simplify the attendance process efficiently. Face Recognition is wrong a biometric technique that allows an authentic computer or machine to can recognize human faces. Local Binary Pattern Histogram (LBPH) is features for classifying combined with histograms and constitute a new technique of the LBP (Local Binary Pattern) method to change performance facial recognition results. LBP is generally designed for texture recognition. LBPH is a suitable method for facial image recognition implemented on the website because the system is able to detect faces with good to 100% accuracy. The attendance system can recognize faces that have been registered detected with an average accuracy of 60%.
| Dosen Pembimbing: | UNSPECIFIED | UNSPECIFIED |
|---|---|
| Item Type: | Thesis (Skripsi (S1)) |
| Uncontrolled Keywords: | Absensi Mahasiswa, Face Recognition, LBPH, Website, Python |
| Subjects: | T Technology > T Technology (General) T Technology > T Technology (General) > T201 Patents. Trademarks |
| Divisions: | Faculty of Engineering |
| Depositing User: | ft . userft |
| Date Deposited: | 22 Feb 2024 05:17 |
| Last Modified: | 22 Feb 2024 05:17 |
| URI: | https://eprints.umpo.ac.id/id/eprint/13199 |
