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All Journal Sainsbertek Jurnal Ilmiah Sains & Teknologi
Asterik Rafael Winoto
Mahasiswa Teknik Informatika, Fakultas Sains & Teknologi, Universitas Ma Chung
Arts Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Medicine & Pharmacology Public Health

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RANCANG BANGUN APLIKASI PRESENSI DENGAN MEDIA SUARA MENGGUNAKAN MFCC DAN ANN BERBASIS ANDROID Asterik Rafael Winoto
Sainsbertek Jurnal Ilmiah Sains & Teknologi Vol. 1 No. 1 (2020): September - Sainsbertek Jurnal Ilmiah Sains & Teknologi
Publisher : Fakultas Sains Dan Teknologi Universitas Ma Chung

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Abstract

Abstrak Suara manusia memiliki keragaman masing-masing di tiap individu sehingga dapat digunakan sebagai media presensi. Dengan teknologi Mel-Frequency Cepstral Coefficient dan Artificial Neural Network berbasis Android diharapkan dapat membuat sebuah presensi suara yang mudah, aman, dan murah. Dataset dikumpulkan dari 6 pegawai toko Jersey Bike Malang dengan 50 data suara per pegawai sepanjang 3 detik. Data suara diproses oleh MFCC untuk memperoleh nilai koefisien Cepstral dan digunakan sebagai Input ANN. Uji coba arsitektur model di lakukan beberapa kali hingga didapatkan 5 model terbaik yang selanjutnya digunakan untuk uji coba akurasi terhadap tambahan suara Noise. Hasil dari uji coba dipilih 1 model arsitektur yang memiliki akurasi Testing tertinggi yaitu dengan 5 Hidden Layer dengan jumlah Node sebanyak 1000 dan 180 sesuai dengan urutan Layer dengan 10 data Noise yang menghasilkan persentase akurasi Train sebesar 91.6 % dan persentase akurasi Test sebesar 91.6% Kata kunci: Presensi, Mel-Frequency Cepstral Coefficient, Artificial Neural Network, Android. Abstract Human voice has a diversity of each in each individual so that it can be used as a presence media. With Android-Based MelFrequency Cepstral Coefficient technology and Artificial Neural Networks, it is expected to produce easy, safe, and inexpensive sounds. The dataset was collected from 6 Jersey Bike Malang shop employees with 50 voice data per employee for 3 seconds. Voice data is processed by the MFCC to obtain the Cepstral coefficient and is used as ANN Input. Model architectural trials were carried out several times to obtain the 5 best models which will then be used for additional sound verification verification trials. The results of testing select 1 architectural model that has the highest accuracy of Testing, namely 5 Hidden Layers with the number of Nodes as many as 1000 and 180 in accordance with the order of the Layer with 10 Noise data, which results in a Train accuracy percentage of 91.6% and can be tested correctly amounted to 91.6%. Keywords: Presence, Mel-Frequency Cepstral Coefficient, Artificial Neural Networks, Android.
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