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ITS » Master Theses » Manajemen Teknologi Informasi-S2 MMT
Posted by aguss at 08/12/2008 07:02:33  •  23764 Views


KLASIFIKASI DATA TIME SERIES MENGGUNAKAN METODE RUANG FASE TEREKONSTRUKSI

TIME SERIES DATA CLASSIFICATION USING RECONSTRUCTED PHASE SPACE METHOD

Author :
JUSUF, MUHAMMAD 




ABSTRAK

Ruang Fase Terekonstruksi RPS merupakan matriks penyisipan waktu tunda sehingga ruang yang dihasilkan ekuivalen secara topologi dengan sistem yang asli. RPS banyak digunakan untuk klasifikasi pola karena keampuhannya dalam menangkap informasi nonlinear. Dalam tesis ini diimplementasikan RPS pada pemodelan time series dengan memanfaatkan pembelajaran Gaussian Mixture Model GMM untuk menciptakan model nonlinear yang akurat. Metode ini terdiri dari tiga tahap utama yaitu analisis data pembelajaran GMM dan klasifikasi. Tahap analisis data meliputi normalisasi sinyal dan perhitungan parameter RPS yaitu waktu tunda lag dan dimensi. Tahap pembelajaran GMM memanfaatkan kedua parameter tersebut dan jumlah mixture untuk membangun GMM yang parameternya diestimasi menggunakan algoritma Expectation Maximization EM. Tahap klasifikasi dilakukan untuk menentukan akurasi dari model yang telah dibangun. Berdasarkan uji coba yang dilakukan dapat ditunjukkan bahwa metode RPS mampu untuk mengklasifikasi data uji coba hingga mencapai tingkat akurasi di atas 80. Klasifikasi terhadap 6 data time series yaitu data Time Series I sd VI menghasilkan rata-rata tingkat akurasi berturut-turut sebagai berikut 73.96 83.3 87.5 90.3 93.7 dan 97.43 untuk jumlah mixture 4 sampai dengan 32.


ABSTRACT

Reconstructed phase space RPS is lag embbeded matrix that can be used to reconstruct a space topologically equivalent to the original system. RPS has been used for pattern classification because of its capability to capture nonlinear information. RPS is implementated in time series modelling using the Gaussian mixture model GMM learning to create accurate nonlinier model. This method has three steps. The first step data analysis includes normalizing the signals and estimating the time lag and dimension of the RPS. The second step is the GMM learning using time lag dimension and the number of mixture building GMM in which the parameters was estimated using Expectation Maximization algorithm. The final step is signal classification to determine the accuracy of the model. Based on test the result shows that RPS is capable to classify the data with accuracy level up to 80 . Classificaton for 6 time series data that is Time Series I until VI has produced average accuracy level respectively as follows 73.96 83.3 87.5 90.3 93.7 and 97.43 . This is produced for numbers of mixtures 4 until 32.



KeywordsTime series, Klasifikasi ; Ruang fase terekonstruksi ; Gaussian mixture model.
 
Subject:  Pengolahan data elektronis
Contributor
  1. Rully Soelaiman, SKom., MKom
Date Create: 08/12/2008
Type: Text
Format: pdf
Language: Indonesian
Identifier: ITS-Master-3100007030119
Collection ID: 3100007030119
Call Number: RTMT 359.83 Suh s


Source
Master Thesis, Management Information Technology, RTMT 359.83 Suh s, 2007

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