UMM Institutional Repository


Budiman, Andri (2011) SISTEM OTOMATISASI ABSENSI DENGAN PENGENALAN SUARA MANUSIA. Other thesis, University of Muhammadiyah Malang.

Full text not available from this repository.


In attendance system transformation manual to automatic, both institutions and companies have adopted digital attendance system that uses fingerprint detection, the palm of the hand or face recognition. Such systems are a very advanced technology, but still has flaws in the system in terms of economic sensordan can be said is still very expensive. Unlike voice recognition system which is a process in which the computer can recognize human speech manually, these systems rely on software running on a computer and a microphone as a voice that would include having a lower economic value. Today's voice recognition technology is more applicable, among others, control the transport conveyor runs at a factory, human interaction with computers, and detect the sound of someone on the phone because the voice signal is very easy to detect. In the process of voice recognition, an algorithm is very supportive of system performance. One example algorithm that can be used is the multilayer perceptron back propagation learning algorithm. This algorithm developed by Rumelhart, Hinton and Williams in the years around 1986. Algorithm which is one type of neural architecture that has a right to be able to perform classification, recognition, and prediction with minimum error rate.

Item Type: Thesis (Other)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering > Department of Informatics (55201)
Depositing User: Halimatus Zahroh
Date Deposited: 16 Feb 2015 13:58
Last Modified: 16 Feb 2015 13:58

Actions (login required)

View Item View Item
UMM Official

© 2008 UMM Library. All Rights Reserved.
Jl. Raya Tlogomas No 246 Malang East Java Indonesia - Phone +62341464318 ext. 150, 151 - Fax +62341464101
E-Mail : infopus[at] - Website : - Online Catalog : - Repository :

Web Analytics

UMM Institutional Repository is powered by :
EPrints Logo