Pemanfaatan Pengenalan Wajah Untuk Prediksi Umur Calon Peserta Didik Baru SMP Dengan AWS Rekognition

Authors

  • Fransiskus Lucky Edo Marbun UNIVERSITAS PRIMA INDONESIA
  • Anugrah Christopher Sinaga UNIVERSITAS PRIMA INDONESIA
  • Hendra Pasaribu UNIVERSITAS PRIMA INDONESIA
  • Iyos Saputra Tambun UNIVERSITAS PRIMA INDONESIA
  • Jhon Kelvin Tarigan UNIVERSITAS PRIMA INDONESIA

DOI:

https://doi.org/10.30865/mib.v8i3.8129

Keywords:

Face Recognition, Age Prediction, AWS Rekognition, Age Verification, AI Technology

Abstract

Verification of the age of new prospective students for middle school is a crucial step that often faces significant challenges when conducted manually, as it is time-consuming and prone to errors. This research aims to address these issues by utilizing facial recognition technology through AWS Rekognition to automatically and accurately predict the age of prospective students. In this study, facial data of new prospective students were collected and analyzed using AWS Rekognition to estimate their ages. The predicted results were then compared with the actual age data to assess the accuracy and effectiveness of the applied method. This study found that AWS Rekognition was able to provide age predictions with a high level of accuracy, achieving a Mean Absolute Error (Low Age) of 2.252032520325203 and a Mean Absolute Error (High Age) of 4.048780487804878, making it an effective tool for age verification. The use of this facial recognition technology not only increases the efficiency and speed of verification but also reduces the potential for human error. Thus, the implementation of AWS Rekognition can be an innovative and reliable solution for improving the quality of the age verification process for new prospective students.

References

M. A. Safitri and R. Yusran, “Evaluasi Kebijakan Penerimaan Peserta Didik Baru (PPDB) dengan Sistem Zonasi pada SMA Negeri di Kota Padang,†Journal of Civic Education, vol. 4, no. 1, pp. 92–100, 2021, doi: 10.24036/jce.v4i1.436.

R. K. Filsafat, S. Pendidikan, K. Pendidikan, and F. Uny, “IMPLEMENTASI KEBIJAKAN PENERIMAAN PESERTA DIDIK BARU (PPDB) SISTEM ZONASI PADA JENJANG SMP NEGERI DI KOTA YOGYAKARTA THE IMPLEMENTATION OF ZONATION SYSTEM FOR NEW STUDENT ADMISSIONS (PPDB) POLICY AT SMP NEGERI IN YOGYAKARTA.â€

R. Leonando Aoetpah, B. Ismanto, M. Administrasi Pendidikan, and U. Kristen Satya Wacana, “IMPLEMENTASI PPDB ZONASI DALAM PERSPEKTIF TEORI PERUBAHAN KURT LEWIN,†SAP (Susunan Artikel Pendidikan, vol. 7, no. 2, 2022.

A. A. M. I. Puji Suryanti, “EFEKTIVITAS PELAKSANAAN SISTEM ZONASI DALAM PENERIMAAN PESERTA DIDIK BARU (PPDB) SERTA PENGARUHNYA TERHADAP UPAYA MANAJEMEN MUTU PENDIDIKAN BERDASARKAN ASAS KEADILAN DI SMA NEGERI KABUPATEN KLATEN,†JURNAL CANDI, vol. 20, no. 1, pp. 111–126, Mar. 2020.

S. A. Salamah and S. Anjaryani, “IMPLEMENTASI PENERIMAAN PESERTA DIDIK SEKOLAH KHUSUS OLAHRAGAWAN SMP/SMA NEGERI RAGUNAN JAKARTA,†SAP (Susunan Artikel Pendidikan, vol. 8, no. 3, 2024.

A. L. Perdana and S. Suharni, “Analisis Adopsi Inovasi Teknologi Informasi Menggunakan Innovation and Diffusion Theory (IDT) Pada PPDB Online SMKN 3 Gowa,†Jurnal Pendidikan dan Teknologi Indonesia, vol. 1, no. 7, pp. 269–274, Jul. 2021, doi: 10.52436/1.jpti.57.

S. T. Nurjaningsih and A. Qonita, “Jurnal Tata Kelola Pendidikan MANAJEMEN PENERIMAAN PESERTA DIDIK BARU (PPDB) SISTEM ZONASIâ€, [Online]. Available: https://ejournal.upi.edu/index.php/jtkp

A. Arifandi, “Jurnal Terapan Sains & Teknologi IDENTIFIKASI DAN PREDIKSI UMUR SERTA JENIS KELAMIN BERDASARKAN CITRA WAJAH MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK(CNN),†Fakultas Sains dan Teknologi-Universitas PGRI Kanjuruhan Malang, vol. 4, no. 2, p. 2022.

I. Budi Trisno and M. Agung Raharja, “WEBINAR ARTIFICIAL INTELLIGENCE DAN MACHINE LEARNING,†JPM Jurnal Pengabdian Mandiri, vol. 2, no. 11, 2023, [Online]. Available: http://bajangjournal.com/index.php/JPM

J. Afrita, “Peran Artificial Intelligence dalam Meningkatkan Efisiensi dan Efektifitas Sistem Pendidikan,†COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat, vol. 2, no. 12, pp. 3181–3187, Apr. 2023, doi: 10.59141/comserva.v2i12.731.

B. A. Septyanto, “Implementasi Face Recognition Berbasis Deep Neural Network Sebagai Sistem Kendali Pada Quadcopter Implementation Of Face Recognition Based On Deep Neural Network As Control System On Quadcopter,†vol. 8, no. 6, pp. 3036–3050, 2022.

E. Purwawijaya, R. N. Singarimbun, and H. Pasaribu, “Implementasi Face Recognition Pada Absensi Karyawan Menggunakan Local Binary Pattern Histogram dan SHA 256 bit,†Jurnal Media Informatika Budidarma, vol. 6, no. 4, p. 2383, 2022, doi: 10.30865/mib.v6i4.4923.

M. S. A. F. A. Risky Aditia, “Implementation of Opencv Face Recognition in Real-Time Age and Gender Detection Using Python with Classification Method,†Jurnal Garuda Pengabdian Kepada Masyarakat, vol. 1, no. 2, pp. 49–55, Sep. 2023.

N. J. Lailatul Fitria and M. Maksin, “Strategi Implementasi Face Recognition sebagai Inovasi dalam Meningkatkan Akurasi Verifikasi Peserta Seleksi ASN,†Jurnal Studi Inovasi, vol. 2, no. 1, pp. 54–62, Jan. 2022, doi: 10.52000/jsi.v2i1.76.

F. Adiputra and F. Umam, “Presensi Wireless Otomatis menggunakan Face Recognition,†Rekayasa, vol. 15, no. 3, pp. 386–397, Dec. 2022, doi: 10.21107/rekayasa.v15i3.19762.

M. A. Joodi, M. H. Saleh, and D. J. Khadhim, “Proposed Face Detection Classification Model Based on Amazon Web Services Cloud (AWS),†Journal of Engineering, vol. 29, no. 4, pp. 176–206, Apr. 2023, doi: 10.31026/j.eng.2023.04.12.

L. Li, X. Mu, S. Li, and H. Peng, “A Review of Face Recognition Technology,†IEEE Access, vol. 8, pp. 139110–139120, 2020, doi: 10.1109/ACCESS.2020.3011028.

A. A. Suryanto, A. Muqtadir, and S. Artikel, “PENERAPAN METODE MEAN ABSOLUTE ERROR (MEA) DALAM ALGORITMA REGRESI LINEAR UNTUK PREDIKSI PRODUKSI PADI Info Artikel : ABSTRAK,†no. 1, p. 11, 2019.

D. Hindarto, “Indonesian Culinary Application System Design with UML Method,†Journal of Computer Networks, Architecture and High Performance Computing, vol. 5, no. 2, pp. 612–622, Aug. 2023, doi: 10.47709/cnahpc.v5i2.2675.

A. T. Nurani, A. Setiawan, and B. Susanto, “Perbandingan Kinerja Regresi Decision Tree dan Regresi Linear Berganda untuk Prediksi BMI pada Dataset Asthma,†Jurnal Sains dan Edukasi Sains, vol. 6, no. 1, pp. 34–43, May 2023, doi: 10.24246/juses.v6i1p34-43.

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Published

2024-07-31

Issue

Vol. 8 No. 3 (2024): Juli 2024

Section

Articles

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