Sistem Otentikasi Biometrik Berbasis Sinyal EKG Menggunakan Convolutional Neural Network  1 Dimensi

FAUZI FRAHMA TALININGSIH; YUNENDAH NUR FUâ€™ADAH; SYAMSUL RIZAL; ACHMAD RIZAL; MUHAMMAD ADNAN PRAMUDITO

doi:10.26760/mindjournal.v7i1.1-10

Sistem Otentikasi Biometrik Berbasis Sinyal EKG Menggunakan Convolutional Neural Network 1 Dimensi

FAUZI FRAHMA TALININGSIH, YUNENDAH NUR FUâ€™ADAH, SYAMSUL RIZAL, ACHMAD RIZAL, MUHAMMAD ADNAN PRAMUDITO

Sari

ABSTRAK

Biometrik merupakan salah satu analisis karakteristik individu yang saat ini banyak digunakan, seperti sidik jari, pengenalan suara, dan pengenalan wajah. Metode biometrik tersebut masih memiliki kelemahan seperti mudah untuk dimanipulasi. Oleh karena itu, penelitian ini akan menggunakan sinyal Elektrokardiogram (EKG) sebagai salah satu metode biometrik. Sinyal EKG memiliki keunikan pada setiap individu sehingga sulit untuk dimanipulasi. Penelitian ini mengembangkan sistem otentikasi biometrik berbasis sinyal EKG. Data yang digunakan berasal dari ECG-ID database dengan jumlah 90 subjek. Sinyal EKG yang digunakan hanya menggunakan gelombang PQRST sebagai input model Convolutional Neural Network 1 Dimensi (CNN). Hasil akurasi yang diperoleh menunjukkan 92.2%. Dengan demikian, sistem yang dikembangkan memungkinkan digunakan sebagai otentikasi biometrik.

Kata kunci: Biometrik, Sinyal EKG, Convolutional Neural Network

ABSTRACT

Biometrics is analyses individual characteristics that are currently widely used, such as fingerprints, voice recognition, and face recognition. The biometric method still has weaknesses, such as being easy to manipulate. Therefore, this study will use an Electrocardiogram (ECG) signal as a biometric method. The ECG signal is unique to each individual, so it is not easy to manipulate. This study develops a biometric authentication system based on ECG signals. The data used comes from the ECG-ID database with a total of 90 subjects. The ECG signal used only PQRST waves as input for the 1-Dimensional Convolutional Neural Network (CNN) model. The accuracy results obtained show 92.2%. Thus, the developed system allows it to be used as biometric authentication.

Keywords: Biometric, ECG Signal, Convolutional Neural Network

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Referensi

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DOI: https://doi.org/10.26760/mindjournal.v7i1.1-10