Prediksi Data Runtun Waktu Menggunakan Jaringan Syaraf Tiruan
This research concerns about application of artificial neural networks (ANN) for predicting time series data. By modifying perceptron’s activation function with linear function, we got linear networks. In order to predict time series data, these linear networks combine with adaptive LMS algorithm. A...
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| Format: | Article |
| Language: | Indonesian |
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Universitas Jenderal Soedirman
2009-02-01
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| Series: | Dinamika Rekayasa |
| Subjects: | |
| Online Access: | http://dinarek.unsoed.ac.id/jurnal/index.php/dinarek/article/view/20 |
| _version_ | 1829115430466224128 |
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| author | Agung Mubyarto |
| author_facet | Agung Mubyarto |
| author_sort | Agung Mubyarto |
| collection | DOAJ |
| description | This research concerns about application of artificial neural networks (ANN) for predicting time series data. By modifying perceptron’s activation function with linear function, we got linear networks. In order to predict time series data, these linear networks combine with adaptive LMS algorithm. And then we had completed this model with time delay function to accommodate past data in time series. The data that used in the test had varied in frequency and sampling time. Results of the test had shown that the networks work properly to predict the data series. |
| first_indexed | 2024-12-12T16:15:38Z |
| format | Article |
| id | doaj.art-23fdc3acb8724a578fe5bc08db52ba35 |
| institution | Directory Open Access Journal |
| issn | 1858-3075 2527-6131 |
| language | Indonesian |
| last_indexed | 2024-12-12T16:15:38Z |
| publishDate | 2009-02-01 |
| publisher | Universitas Jenderal Soedirman |
| record_format | Article |
| series | Dinamika Rekayasa |
| spelling | doaj.art-23fdc3acb8724a578fe5bc08db52ba352022-12-22T00:19:07ZindUniversitas Jenderal SoedirmanDinamika Rekayasa1858-30752527-61312009-02-0151252920Prediksi Data Runtun Waktu Menggunakan Jaringan Syaraf TiruanAgung Mubyarto0Prodi Teknik Elektro Universitas Jenderal SoedirmanThis research concerns about application of artificial neural networks (ANN) for predicting time series data. By modifying perceptron’s activation function with linear function, we got linear networks. In order to predict time series data, these linear networks combine with adaptive LMS algorithm. And then we had completed this model with time delay function to accommodate past data in time series. The data that used in the test had varied in frequency and sampling time. Results of the test had shown that the networks work properly to predict the data series.http://dinarek.unsoed.ac.id/jurnal/index.php/dinarek/article/view/20Time series data, linear networks, prediction, adaptive filter |
| spellingShingle | Agung Mubyarto Prediksi Data Runtun Waktu Menggunakan Jaringan Syaraf Tiruan Dinamika Rekayasa Time series data, linear networks, prediction, adaptive filter |
| title | Prediksi Data Runtun Waktu Menggunakan Jaringan Syaraf Tiruan |
| title_full | Prediksi Data Runtun Waktu Menggunakan Jaringan Syaraf Tiruan |
| title_fullStr | Prediksi Data Runtun Waktu Menggunakan Jaringan Syaraf Tiruan |
| title_full_unstemmed | Prediksi Data Runtun Waktu Menggunakan Jaringan Syaraf Tiruan |
| title_short | Prediksi Data Runtun Waktu Menggunakan Jaringan Syaraf Tiruan |
| title_sort | prediksi data runtun waktu menggunakan jaringan syaraf tiruan |
| topic | Time series data, linear networks, prediction, adaptive filter |
| url | http://dinarek.unsoed.ac.id/jurnal/index.php/dinarek/article/view/20 |
| work_keys_str_mv | AT agungmubyarto prediksidataruntunwaktumenggunakanjaringansyaraftiruan |