FACE RECOGNITION (PENGENALAN WAJAH) UNTUK SISTEM PRESENSI BERBASIS WEBSITE PADA LABORATORIUM SOFTWARE UNIVERSITAS ANDI DJEMMA
Abstract
Abstrak. Penelitian ini dibuat sebagai upaya untuk mensuport peningkatan efisiensi dan juga modernisasi melakukan presensi di Laboratorium Software Fakultas Teknik Universitas Andi Djemma. Sejalan dengan berkembangnya teknologi informasi, maka dibutuhkan suatu sistem presensi yang dapat membantu mempermudah pencatatan kehadiran yang presisi serta aman. Oleh karena itu, tujuan penelitian ini untuk membuat rangan bangun merealisasikan sistem presensi berbasis web yang menggunakan teknologi pengecekan wajah atau yang biasa disebut face recognition. Metode penelitian yang dilakukan yaitu Research and Development (R&D) dengan model pengembangan Waterfall yang meliputi tahap analisis, perancangan, kode, uji, dan implementasi system. Sistem ini dikembangkan menggunakan HTML, PHP, dan Javascript dengan pustaka face-api.js berbasis TensorFlowJS dan model deep learning pra-terlatih FaceNet dan DeepFace. Pengujian sistem menggunakan metode blackbox untuk memastikan seluruh fungsi sistem berjalan sesuai kebutuhan pengguna. Berdasarkan hasil penelitian, sistem presensi yang berkembang medeteksi dan mengenali wajah secara real-time memliki tingkat akurasi yang baik dan mendukung pengelolaan data presensi terintegrasi. Diharapkan dengan adanya sistem ini dapat bermanfaat dalam hal meningkatkan efektivitas pengelolaan presensi dan mendukung transformasi digital lingkungan laboratorium.
Abstract. This research was conducted as an effort to support efficiency improvement and modernization of attendance recording in the Software Laboratory of the Faculty of Engineering, Andi Djemma University. In line with the development of information technology, an attendance system is needed to facilitate accurate and secure attendance recording. Therefore, the purpose of this research is to develop a web-based attendance system that uses face recognition technology. The research method used is Research and Development with a Waterfall development model, which includes the stages of analysis, design, coding, testing, and system implementation. This system was developed using HTML, PHP, and Javascript with the face-api.js library based on TensorFlowJS and pre-trained deep learning models FaceNet and DeepFace. The system was tested using the black box method to ensure that all system functions ran according to user requirements. Based on the research results, the developed attendance system detects and recognizes faces in real-time with a good level of accuracy and supports integrated attendance data management. It is hoped that this system will be useful in improving the effectiveness of attendance management and supporting the digital transformation of the laboratory environment.
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