IMPLEMENTASI METODE FAST FOURIER TRANSFORM DAN KNEAREST NEIGHBOR DALAM APLIKASI IDENTIFIKASI SUARA BERBASIS ANDROID
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
RUSLANI, RAHMANIA
Haris, Abdul
Cahyaningtyas, Rizqia
Haris, Abdul
Cahyaningtyas, Rizqia
Subject
Teknik Informatika
Datestamp
2022-09-22 07:14:09
Abstract :
The authentication systems that are developing at this time include eye iris recognition, fingerprint recognition and voice recognition. The introduction of sound patterns can be used as authentication because the voice patterns of every human being are varied and different. The frequency produced by each human is different depending on the sex, age also affects the results of the sound pattern. This research is only focused on the process of identifying the sound on the Smartphone that entered the training data stored on the Smartphone drive by using the voice identification application that entered will be converted to wav extension. The FFT and K-NN methods are able to assist in matching new sounds or test sounds to training sounds. The K-NN method requires training data and test data in the form of a discrete frequency signal value to be matched, and the FFT method can produce the discrete frequency value. This voice identification application uses the Waterfall method as a software development system. This study produces an output in the form of a name that matches the name with the closest distance between the training data and the input data and produces the closest distance value between the input sound and the training data sound. After testing with proximity testing as many as 6 different input data with 4 training data so that this application has an accuracy value of 83% and an eror of 16%.
The authentication systems that are developing at this time include eye iris recognition, fingerprint recognition and voice recognition. The introduction of sound patterns can be used as authentication because the voice patterns of every human being are varied and different. The frequency produced by each human is different depending on the sex, age also affects the results of the sound pattern. This research is only focused on the process of identifying the sound on the Smartphone that entered the training data stored on the Smartphone drive by using the voice identification application that entered will be converted to wav extension. The FFT and K-NN methods are able to assist in matching new sounds or test sounds to training sounds. The K-NN method requires training data and test data in the form of a discrete frequency signal value to be matched, and the FFT method can produce the discrete frequency value. This voice identification application uses the Waterfall method as a software development system. This study produces an output in the form of a name that matches the name with the closest distance between the training data and the input data and produces the closest distance value between the input sound and the training data sound. After testing with proximity testing as many as 6 different input data with 4 training data so that this application has an accuracy value of 83% and an eror of 16%.
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