[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"ssr-repo-dspace000004101":3,"ssr-repo-divisions":79,"menu-footer-id":265,"menu-main-id":326},{"id":4,"title":5,"authors":6,"files":9,"advisors":36,"authors_detail":37,"advisors_detail":44,"publisher":45,"publish_place":46,"language":47,"type":48,"type_slug":49,"year":50,"department":51,"study_program":52,"keywords":53,"keywords_detail":61,"abstract":76,"abstract_html":77,"url":78},"repo-dspace000004101","Implementasi Sistem Face Recognition Menggunakan Algoritma Convolutional Neural Network Sebagai Alternatif Akses Lemari Untuk Peminjaman Perangkat. (Studi Kasus PT. Telkom Indonesia-Malang)",[7,8],"Labib Anan Makrub","M. 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Penelitian ini membahas implementasi sistem face recognition menggunakan algoritma Convolutional Neural Network (CNN) sebagai alternatif akses lemari untuk peminjaman perangkat, dengan memanfaatkan sistem RFID untuk pelacakan perangkat serta integrasi sistem notifikasi Telegram untuk pengingat pengembalian perangkat. Sistem ini menggabungkan kemampuan CNN dalam mengenali wajah secara akurat dengan teknologi RFID untuk mengidentifikasi dan melacak perangkat yang dipinjam. Selain itu, sistem ini dilengkapi dengan fitur notifikasi yang mengirimkan pesan pengingat ke grup Telegram untuk memastikan pengembalian perangkat tepat waktu. Tujuan penelitian ini adalah meningkatkan keamanan dan efisiensi dalam proses peminjaman perangkat di PT. Telkom Indonesia-Malang serta mengoptimalkan pengelolaan inventaris perangkat dengan menyediakan sistem yang lebih terintegrasi dan otomatis dalam pencatatan dan pemantauan peminjaman perangkat. Proses implementasi meliputi pengumpulan data wajah, pelatihan model CNN, integrasi teknologi face recognition dengan mekanisme solenid door lock, penggunaan tag RFID untuk pelacakan perangkat, pengaturan sistem notifikasi Telegram, serta pemantauan data peminjaman dan akses lemari melalui website. Prosedur sistem ini dengan cara sejajarkan wajah dengan kamera kemudian tekan tombol push button. Setelah itu wajah diprediksi oleh sistem diterima atau ditolak, ketika diterima maka pintu lemari akan terbuka otomatis. Pinjam perangkat yang dibutuhkan dengan cara scan RFID tag ke RFID reader, dan untuk warning sistem melalui pesan telegram yang dikirim ke grup telegram. Hasil pengujian menunjukkan bahwa sistem ini dapat mengenali wajah dengan tingkat akurasi 70% terhadap variasi jarak wajah terhadap kamera dan tingkat akurasi 80% terhadap variasi intensitas cahaya. Mengidentifikasi perangkat yang dipinjam secara efisien, dan memberikan pengingat pengembalian perangkat secara efektif melalui notifikasi Telegram. Implementasi ini diharapkan dapat diaplikasikan di berbagai institusi yang memerlukan sistem akses dan pengelolaan peminjaman perangkat yang canggih, aman, dan efisien, serta mengurangi masalah kehilangan perangkat yang dapat mempengaruhi kinerja maintenance.","\u003Cp>Penggunaan teknologi pengenalan wajah (face recognition) dan Radio Frequency Identification (RFID) dalam sistem keamanan semakin berkembang pesat. Penelitian ini membahas implementasi sistem face recognition menggunakan algoritma Convolutional Neural Network (CNN) sebagai alternatif akses lemari untuk peminjaman perangkat, dengan memanfaatkan sistem RFID untuk pelacakan perangkat serta integrasi sistem notifikasi Telegram untuk pengingat pengembalian perangkat. Sistem ini menggabungkan kemampuan CNN dalam mengenali wajah secara akurat dengan teknologi RFID untuk mengidentifikasi dan melacak perangkat yang dipinjam. Selain itu, sistem ini dilengkapi dengan fitur notifikasi yang mengirimkan pesan pengingat ke grup Telegram untuk memastikan pengembalian perangkat tepat waktu. Tujuan penelitian ini adalah meningkatkan keamanan dan efisiensi dalam proses peminjaman perangkat di PT. Telkom Indonesia-Malang serta mengoptimalkan pengelolaan inventaris perangkat dengan menyediakan sistem yang lebih terintegrasi dan otomatis dalam pencatatan dan pemantauan peminjaman perangkat. Proses implementasi meliputi pengumpulan data wajah, pelatihan model CNN, integrasi teknologi face recognition dengan mekanisme solenid door lock, penggunaan tag RFID untuk pelacakan perangkat, pengaturan sistem notifikasi Telegram, serta pemantauan data peminjaman dan akses lemari melalui website. Prosedur sistem ini dengan cara sejajarkan wajah dengan kamera kemudian tekan tombol push button. Setelah itu wajah diprediksi oleh sistem diterima atau ditolak, ketika diterima maka pintu lemari akan terbuka otomatis. Pinjam perangkat yang dibutuhkan dengan cara scan RFID tag ke RFID reader, dan untuk warning sistem melalui pesan telegram yang dikirim ke grup telegram. Hasil pengujian menunjukkan bahwa sistem ini dapat mengenali wajah dengan tingkat akurasi 70% terhadap variasi jarak wajah terhadap kamera dan tingkat akurasi 80% terhadap variasi intensitas cahaya. Mengidentifikasi perangkat yang dipinjam secara efisien, dan memberikan pengingat pengembalian perangkat secara efektif melalui notifikasi Telegram. 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