PENGEMBANGAN FITUR E-LOGBOOK BERBASIS FACE RECOGNITION PADA WEBSITE C2UKALTARA.ID | ELECTRONIC THESES AND DISSERTATION
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PENGEMBANGAN FITUR E-LOGBOOK BERBASIS FACE RECOGNITION PADA WEBSITE C2UKALTARA.ID

Pengarang : Andri Efendy - Personal Name;

Perpustakaan UBT : Universitas Borneo Tarakan., 2025
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Abstract

Perkembangan teknologi informasi mendorong dunia pendidikan untuk terus berinovasi salah satunya melalui program Merdeka Belajar Kampus Merdeka (MBKM). Salah satu program MBKM adalah magang mandiri, yang memungkinkan mahasiswa merancang pengalaman belajar sesuai minat dan kebutuhan. C2UKaltara merupakan platform yang mendukung pelaksanaan magang mandiri magang mandiri, namun fitur E-Logbook yang ada belum mampu memvalidasi identitas pengguna. Oleh karena itu, penelitian ini mengembangkan fitur E-Logbook berbasis Face Recognition menggunakan metode Convolutional Neural Network (CNN) pada website c2ukaltara.id Teknologi pengenalan wajah diimplementasikan melalui library face-api.js dengan menggunakan Express.js sebagai framework backend. Pengujian sistem dilakukan sebanyak 180 kali dan menghasilkan akurasi sebesar 98,06%, Precision 100%, recall 96%, dan F1 Score 97,96%. Hasil ini menunjukkan bahwa sistem mampu mengenali wajah mahasiswa dengan sangant baik. Pengembangan ini diharapkan mampu meningkatkan efisiensi, akurasi, dan keamanan dalam pendokumentasian aktivitas logbook mahasiswa MBKM

The advancement of information technology has driven the education sector to innovate continuously, one of which is through the Merdeka Belajar Kampus Merdeka (MBKM) program. One of the MBKM programs is independent internships, which allow students to design their learning experiences according to their interests and needs. C2UKaltara is a platform that supports the implementation of independent internships, but the existing E-Logbook feature cannot validate user identities. Therefore, this study developed an E-Logbook feature based on Face Recognition using the Convolutional Neural Network (CNN) method on the c2ukaltara.id website. Face recognition technology was implemented through the face-api.js library using Express.js as the backend framework. The system was tested 180 times and achieved an accuracy of 98.06%, Precision of 100%, Recall of 96%, and F1 Score of 97.96%. These results indicated that the system can recognize students' faces very well. This development will enhance efficiency, accuracy, and security in documenting MBKM students' logbook activities.

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