Rancang Bangun Aplikasi Absensi Pegawai dengan Face Recognition Berbasis Android di PT. Nutech Integrasi
DOI:
https://doi.org/10.32493/jtsi.v7i2.38812Keywords:
machine learning; employee attendance; face recognition; tensorflow; geolocationAbstract
The adaptation of new habits after the COVID-19 pandemic has created a new habit, namely work activities that can be done from home. PT Nutech Intgerasi has new rules as a form of adaptation to new habits, called Flexible Working Arrangements, which is work from home, work from office and onsite work systems. The current attendance system cannot cover these three work systems. Therefore, a new system is needed so that the head unit and the Human Capital & Organization team can continue to record and monitor each employee. The system is built using System development Life Cycle method and face recognition technology, then added Geolocation to record the location, the system was built with the JavaScript programming language. In the initial stage, the application has been implemented in the Business and Product Development department. The application has successfully recorded employee attendance and recorded the location when employees make attendance. For the face recognition feature of the five test data taken, four of them show data accuracy below the euclidean distance threshold value, where the euclidean distance threshold value is 0.40, even one of the test data managed to have an euclidean distance of 0.18 which means that the level of similarity is very high because the smaller the euclidean distance, the higher the level of similarity. In addition, by giving a lower limit value at the face detection stage of 0.90, only faces that are clearly visible and with good lighting are captured.
References
Abdulloh, R. (2018). 7 in 1 Pemrograman Web Tingkat Lanjut. Jakarta : Elex Media Komputindo.
Almabdy, S. & Elrefaei, L. (2019). Deep Convolutional Neural Network-Based Approaches for Face Recognition. Applied Sciences, 9(10), 4397.
Arsal, M., Wardijono, B. A. & Anggraini, D. (2020). Face Recognition Untuk Akses Pegawai Bank Menggunakan Deep Learning Dengan Metode CNN. Teknik Informatika, 6(1), 55-63.
Arwaz, A., Kusumawijaya, T., Putra, R., Putra K. & Saifudin A. (2019). Pengujian Black Box pada Aplikasi Sistem Seleksi Pemenang Tender Menggunakan Teknik Equivalence Partitions. Jurnal Teknologi Sistem Informasi Aplikasi, 2(4), 130-134.
Basurah, M., Swastika, W. & Kelana, O. H. (2023). Implementation of Face Recognition and Liveness Detection System Using Tensorflow.js. Jurnal Informatika Polinema, 9(4), 509-516.
Brownlee, J. (2019). Deep Learning for Computer Vision : Image Classification, Object Detection and Face Recognition in Python. Melbourne: Machine Learning Mastery.
Evelyn, Adipranata, R. & Gunadi, K. (2022). Sistem Presensi Mahasiswa Menggunakan Face Recognition Dengan Metode Facenet Pada Android. Jurnal Infra, 10(2), 56-62.
Liu, Y. C., Kuo, R. L. & Shih, S. R. (2020). COVID-19: The first documented coronavirus pandemic in history. Biomedical Journal, 43(4), 328-333.
Manajang, D. J. P., Sompie, S. R. U. A. & Jacobus, A. (2020). Implementasi Framework Tensorflow Object Detection Dalam Mengklasifikasi Jenis Kendaraan Bermotor. Jurnal Teknik Informatika, 15(3), 171-178.
Momeny, M., Sarram, M. A., Latif, A. M., Sheikhpour, R. & DongZhang, Y. (2021). A Noise Robust Convolutional Neural Network for Image Classification. Results in Engineering, 10, 100-225.
Muliyadia, Anraenia, S. & Hermana. (2022). Rancang Bangun Sistem Absensi Online Berbasis Face Recognition Menggunakan Platform Android, Buletin Sistem Informasi dan Teknologi Islam, 3(1), 7-16.
Setiawan, G. A. & Vania, E. (2022). Praktek Pemrograman C++ dan Python. Semarang : SCU Knowledge Media.
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