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

Fransiskus Lucky Edo Marbun, Anugrah Christopher Sinaga, Hendra Pasaribu, Iyos Saputra Tambun, Jhon Kelvin Tarigan

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.


Keywords


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

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

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