Voice recognition technology is one of biometric technology. Sound is a unique part of the human being which made an individual can be easily distinguished one from another. Voice can also provide information such as gender, emotion, and identity of the speaker. This research will record human voices that pronounce digits between 0 and 9 with and without noise. Features of this sound recording will be extracted using Mel Frequency Cepstral Coefficient (MFCC). Mean, standard deviation, max, min, and the combination of them will be used to construct the feature vectors. This feature vectors then will be classified using Support Vector Machine (SVM). There will be two classification models. The first one is based on the speaker and the other one based on the digits pronounced. The classification model then will be validated by performing 10-fold cross-validation.The best average accuracy from two classification model is 91.83%. This result achieved using Mean + Standard deviation + Min + Max as features.
Komunikasi merupakan salah satu kebutuhan yang penting untuk banyak individu dalam menjalankan aktivitas sehari-hari seperti dalam lingkungan kerja, bisnis maupun pendidikan. Di sisi lain, terdapat masalah dalam melakukan komunikasi secara langsung dikarenakan lokasi tiap pelaku komunikasi yang tidak selalu sama. Perbedaan lokasi peserta individu menciptakan sebuah topik yang menarik untuk diteliti. Tujuan dari penelitian ini adalah mengembangkan aplikasi virtual meeting pada smartphone berbasis sistem operasi Android. Aplikasi virtual meeting ini dapat digunakan untuk chatting dan melakukan meeting conference yang menunjang sarana komunikasi. Aplikasi ini juga dilengkapi dengan fitur-fitur pendukung seperti presentation slide, scribbling board, speaker permission, attention me dan whispering me. Fitur-fitur tersebut memberikan fasilitas komunikasi tatap muka dengan tingkat mobilitas yang tinggi tanpa harus bertemu secara langsung dan komunikasi chat berupa teks dengan satu atau lebih orang.
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