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Perancangan Sistem Pengenalan Wajah Dengan Metode CNN Pada Sistem Keamanan Door Lock Pintu Kamar Hotel

Edu Elektrika Journal · Vol. 12 No. 2 (2025) · hlm. 25–30

DOI
10.15294/0724xq71
ISSN
2723-5602, 2252-7095
Terbit
2025-01-15
Penerbit
LPPM Universitas Negeri Semarang
Bahasa
eng

Abstrak

Room door lock security can be managed using conventional techniques or automated systems. Currently, most hotels in Indonesia, particularly in the Lombok area, utilize card keys for room access. This method is effective and convenient, as guests do not need to carry physical keys. However, there are instances when guests forget to take their card when leaving the room, which complicates re-entering their accommodations. One potential solution is to implement the Convolutional Neural Network (CNN) method for recognition. The CNN process involves four key stages: area detection, feature extraction, matching, and recognition. Tests conducted on this system indicate that the CNN method can accurately recognize provided input images. Out of 10 images tested, 90% were recognized successfully. The results suggest that the CNN method is a promising option for enhancing room lock security

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18 penulis · 30 tautan kepenulisan bersama

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  • Hasanuddin University
  • Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan
  • Universitas Muhammadiyah Palu
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
AMIR YASSIHasanuddin University1
Ifayanti RidwanHasanuddin University1
Syamsuddin NompoAfiliasi tidak diketahui1
R IsnainiAfiliasi tidak diketahui1
N SyamAfiliasi tidak diketahui1
RinduwatiHasanuddin University1
IswantoHasanuddin University1
. RinduwatiAfiliasi tidak diketahui1
Muh. Yusril LatifAfiliasi tidak diketahui1
. SyamsuddinUniversitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan1
S. HasanAfiliasi tidak diketahui1
A. AsrianieAfiliasi tidak diketahui1
EkaAfiliasi tidak diketahui1
DelaUniversitas Muhammadiyah Palu1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/eduel/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Perancangan Sistem Pengenalan Wajah Dengan Metode CNN Pada Sistem Keamanan Door Lock Pintu Kamar Hotel</dc:title>
	<dc:creator xml:lang="en">Anwariyah, Komariyuli</dc:creator>
	<dc:creator xml:lang="en">Hariani, Yuni Sulpia</dc:creator>
	<dc:creator xml:lang="en">Budiman</dc:creator>
	<dc:creator xml:lang="en">MZ, Dedy Sofian</dc:creator>
	<dc:subject xml:lang="en">Face recognition, convolution neural network, door lock, Image Processing</dc:subject>
	<dc:description xml:lang="en">Room door lock security can be managed using conventional techniques or automated systems. Currently, most hotels in Indonesia, particularly in the Lombok area, utilize card keys for room access. This method is effective and convenient, as guests do not need to carry physical keys. However, there are instances when guests forget to take their card when leaving the room, which complicates re-entering their accommodations. One potential solution is to implement the Convolutional Neural Network (CNN) method for recognition. The CNN process involves four key stages: area detection, feature extraction, matching, and recognition. Tests conducted on this system indicate that the CNN method can accurately recognize provided input images. Out of 10 images tested, 90% were recognized successfully. The results suggest that the CNN method is a promising option for enhancing room lock security </dc:description>
	<dc:publisher xml:lang="en">LPPM Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-01-15</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/eduel/article/view/10981</dc:identifier>
	<dc:identifier>10.15294/0724xq71</dc:identifier>
	<dc:source xml:lang="en">Edu Elektrika Journal; Vol. 12 No. 2 (2024); 25-30</dc:source>
	<dc:source>2723-5602</dc:source>
	<dc:source>2252-7095</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/eduel/article/view/10981/2763</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Edu Elektrika Journal</dc:rights>
</oai_dc:dc>